From fbf4763a4076163c0561ea38ebc5c922a44a6aa8 Mon Sep 17 00:00:00 2001
From: jessegrabowski <jessegrabowski@gmail.com>
Date: Thu, 18 Sep 2025 18:56:16 -0500
Subject: [PATCH 1/5] Add more options to DADVI minimization

---
 pymc_extras/inference/dadvi/dadvi.py          | 148 +++++++++++-------
 .../inference/laplace_approx/find_map.py      |  32 +---
 pymc_extras/inference/laplace_approx/idata.py |  24 ++-
 .../laplace_approx/scipy_interface.py         |  54 ++++++-
 tests/inference/dadvi/__init__.py             |   0
 tests/inference/dadvi/test_dadvi.py           | 137 ++++++++++++++++
 .../inference/laplace_approx/test_find_map.py |   2 +-
 7 files changed, 301 insertions(+), 96 deletions(-)
 create mode 100644 tests/inference/dadvi/__init__.py
 create mode 100644 tests/inference/dadvi/test_dadvi.py

diff --git a/pymc_extras/inference/dadvi/dadvi.py b/pymc_extras/inference/dadvi/dadvi.py
index eb4ef98c5..899cb4472 100644
--- a/pymc_extras/inference/dadvi/dadvi.py
+++ b/pymc_extras/inference/dadvi/dadvi.py
@@ -13,12 +13,18 @@
     apply_function_over_dataset,
     coords_and_dims_for_inferencedata,
 )
+from pymc.blocking import RaveledVars
 from pymc.util import RandomSeed, get_default_varnames
 from pytensor.tensor.variable import TensorVariable
 
+from pymc_extras.inference.laplace_approx.idata import (
+    add_data_to_inference_data,
+    add_optimizer_result_to_inference_data,
+)
 from pymc_extras.inference.laplace_approx.laplace import unstack_laplace_draws
 from pymc_extras.inference.laplace_approx.scipy_interface import (
-    _compile_functions_for_scipy_optimize,
+    scipy_optimize_funcs_from_loss,
+    set_optimizer_function_defaults,
 )
 
 
@@ -29,17 +35,17 @@ def fit_dadvi(
     n_draws: int = 1000,
     keep_untransformed: bool = False,
     optimizer_method: minimize_method = "trust-ncg",
-    use_grad: bool = True,
-    use_hessp: bool = True,
-    use_hess: bool = False,
+    use_grad: bool | None = None,
+    use_hessp: bool | None = None,
+    use_hess: bool | None = None,
+    gradient_backend: str = "pytensor",
+    compile_kwargs: dict | None = None,
     **minimize_kwargs,
 ) -> az.InferenceData:
     """
-    Does inference using deterministic ADVI (automatic differentiation
-    variational inference), DADVI for short.
+    Does inference using Deterministic ADVI (Automatic Differentiation Variational Inference), DADVI for short.
 
-    For full details see the paper cited in the references:
-    https://www.jmlr.org/papers/v25/23-1015.html
+    For full details see the paper cited in the references: https://www.jmlr.org/papers/v25/23-1015.html
 
     Parameters
     ----------
@@ -47,46 +53,45 @@ def fit_dadvi(
         The PyMC model to be fit. If None, the current model context is used.
 
     n_fixed_draws : int
-        The number of fixed draws to use for the optimisation. More
-        draws will result in more accurate estimates, but also
-        increase inference time. Usually, the default of 30 is a good
-        tradeoff.between speed and accuracy.
+        The number of fixed draws to use for the optimisation. More draws will result in more accurate estimates, but
+        also increase inference time. Usually, the default of 30 is a good tradeoff between speed and accuracy.
 
     random_seed: int
-        The random seed to use for the fixed draws. Running the optimisation
-        twice with the same seed should arrive at the same result.
+        The random seed to use for the fixed draws. Running the optimisation twice with the same seed should arrive at
+        the same result.
 
     n_draws: int
         The number of draws to return from the variational approximation.
 
     keep_untransformed: bool
-        Whether or not to keep the unconstrained variables (such as
-        logs of positive-constrained parameters) in the output.
+        Whether or not to keep the unconstrained variables (such as logs of positive-constrained parameters) in the
+        output.
 
     optimizer_method: str
-        Which optimization method to use. The function calls
-        ``scipy.optimize.minimize``, so any of the methods there can
-        be used. The default is trust-ncg, which uses second-order
-        information and is generally very reliable. Other methods such
-        as L-BFGS-B might be faster but potentially more brittle and
-        may not converge exactly to the optimum.
+        Which optimization method to use. The function calls ``scipy.optimize.minimize``, so any of the methods there
+        can be used. The default is trust-ncg, which uses second-order information and is generally very reliable.
+        Other methods such as L-BFGS-B might be faster but potentially more brittle and may not converge exactly to
+        the optimum.
+
+    gradient_backend: str
+        Which backend to use to compute gradients. Must be one of "jax" or "pytensor". Default is "pytensor".
+
+    compile_kwargs: dict, optional
+        Additional keyword arguments to pass to `pytensor.function`
 
     minimize_kwargs:
-        Additional keyword arguments to pass to the
-        ``scipy.optimize.minimize`` function. See the documentation of
+        Additional keyword arguments to pass to the ``scipy.optimize.minimize`` function. See the documentation of
         that function for details.
 
-    use_grad:
-        If True, pass the gradient function to
-        `scipy.optimize.minimize` (where it is referred to as `jac`).
+    use_grad: bool, optional
+        If True, pass the gradient function to `scipy.optimize.minimize` (where it is referred to as `jac`).
 
-    use_hessp:
+    use_hessp: bool, optional
         If True, pass the hessian vector product to `scipy.optimize.minimize`.
 
-    use_hess:
-        If True, pass the hessian to `scipy.optimize.minimize`. Note that
-        this is generally not recommended since its computation can be slow
-        and memory-intensive if there are many parameters.
+    use_hess: bool, optional
+        If True, pass the hessian to `scipy.optimize.minimize`. Note that this is generally not recommended since its
+        computation can be slow and memory-intensive if there are many parameters.
 
     Returns
     -------
@@ -95,16 +100,15 @@ def fit_dadvi(
 
     References
     ----------
-    Giordano, R., Ingram, M., & Broderick, T. (2024). Black Box
-    Variational Inference with a Deterministic Objective: Faster, More
-    Accurate, and Even More Black Box. Journal of Machine Learning
-    Research, 25(18), 1–39.
+    Giordano, R., Ingram, M., & Broderick, T. (2024). Black Box Variational Inference with a Deterministic Objective:
+    Faster, More Accurate, and Even More Black Box. Journal of Machine Learning Research, 25(18), 1–39.
     """
 
     model = pymc.modelcontext(model) if model is None else model
 
     initial_point_dict = model.initial_point()
-    n_params = DictToArrayBijection.map(initial_point_dict).data.shape[0]
+    initial_point = DictToArrayBijection.map(initial_point_dict)
+    n_params = initial_point.data.shape[0]
 
     var_params, objective = create_dadvi_graph(
         model,
@@ -113,31 +117,45 @@ def fit_dadvi(
         n_params=n_params,
     )
 
-    f_fused, f_hessp = _compile_functions_for_scipy_optimize(
-        objective,
-        [var_params],
-        compute_grad=use_grad,
-        compute_hessp=use_hessp,
-        compute_hess=use_hess,
+    use_grad, use_hess, use_hessp = set_optimizer_function_defaults(
+        optimizer_method, use_grad, use_hess, use_hessp
+    )
+
+    f_fused, f_hessp = scipy_optimize_funcs_from_loss(
+        loss=objective,
+        inputs=[var_params],
+        initial_point_dict=None,
+        use_grad=use_grad,
+        use_hessp=use_hessp,
+        use_hess=use_hess,
+        gradient_backend=gradient_backend,
+        compile_kwargs=compile_kwargs,
+        inputs_are_flat=True,
     )
 
-    derivative_kwargs = {}
+    dadvi_initial_point = {
+        f"{var_name}_mu": np.zeros_like(value).ravel()
+        for var_name, value in initial_point_dict.items()
+    }
+    dadvi_initial_point.update(
+        {
+            f"{var_name}_sigma__log": np.zeros_like(value).ravel()
+            for var_name, value in initial_point_dict.items()
+        }
+    )
 
-    if use_grad:
-        derivative_kwargs["jac"] = True
-    if use_hessp:
-        derivative_kwargs["hessp"] = f_hessp
-    if use_hess:
-        derivative_kwargs["hess"] = True
+    dadvi_initial_point = DictToArrayBijection.map(dadvi_initial_point)
 
     result = minimize(
-        f_fused,
-        np.zeros(2 * n_params),
+        f=f_fused,
+        x0=dadvi_initial_point.data,
         method=optimizer_method,
-        **derivative_kwargs,
+        hessp=f_hessp,
         **minimize_kwargs,
     )
 
+    raveled_optimized = RaveledVars(result.x, dadvi_initial_point.point_map_info)
+
     opt_var_params = result.x
     opt_means, opt_log_sds = np.split(opt_var_params, 2)
 
@@ -148,9 +166,29 @@ def fit_dadvi(
     draws = opt_means + draws_raw * np.exp(opt_log_sds)
     draws_arviz = unstack_laplace_draws(draws, model, chains=1, draws=n_draws)
 
-    transformed_draws = transform_draws(draws_arviz, model, keep_untransformed=keep_untransformed)
+    idata = az.InferenceData(
+        posterior=transform_draws(draws_arviz, model, keep_untransformed=keep_untransformed)
+    )
+
+    var_name_to_model_var = {f"{var_name}_mu": var_name for var_name in initial_point_dict.keys()}
+    var_name_to_model_var.update(
+        {f"{var_name}_sigma__log": var_name for var_name in initial_point_dict.keys()}
+    )
+
+    idata = add_optimizer_result_to_inference_data(
+        idata=idata,
+        result=result,
+        method=optimizer_method,
+        mu=raveled_optimized,
+        model=model,
+        var_name_to_model_var=var_name_to_model_var,
+    )
+
+    idata = add_data_to_inference_data(
+        idata=idata, progressbar=False, model=model, compile_kwargs=compile_kwargs
+    )
 
-    return transformed_draws
+    return idata
 
 
 def create_dadvi_graph(
diff --git a/pymc_extras/inference/laplace_approx/find_map.py b/pymc_extras/inference/laplace_approx/find_map.py
index 73ad741df..5e96dfe22 100644
--- a/pymc_extras/inference/laplace_approx/find_map.py
+++ b/pymc_extras/inference/laplace_approx/find_map.py
@@ -7,7 +7,7 @@
 import pymc as pm
 
 from better_optimize import basinhopping, minimize
-from better_optimize.constants import MINIMIZE_MODE_KWARGS, minimize_method
+from better_optimize.constants import minimize_method
 from pymc.blocking import DictToArrayBijection, RaveledVars
 from pymc.initial_point import make_initial_point_fn
 from pymc.model.transform.optimization import freeze_dims_and_data
@@ -24,40 +24,12 @@
 from pymc_extras.inference.laplace_approx.scipy_interface import (
     GradientBackend,
     scipy_optimize_funcs_from_loss,
+    set_optimizer_function_defaults,
 )
 
 _log = logging.getLogger(__name__)
 
 
-def set_optimizer_function_defaults(method, use_grad, use_hess, use_hessp):
-    method_info = MINIMIZE_MODE_KWARGS[method].copy()
-
-    if use_hess and use_hessp:
-        _log.warning(
-            'Both "use_hess" and "use_hessp" are set to True, but scipy.optimize.minimize never uses both at the '
-            'same time. When possible "use_hessp" is preferred because its is computationally more efficient. '
-            'Setting "use_hess" to False.'
-        )
-        use_hess = False
-
-    use_grad = use_grad if use_grad is not None else method_info["uses_grad"]
-
-    if use_hessp is not None and use_hess is None:
-        use_hess = not use_hessp
-
-    elif use_hess is not None and use_hessp is None:
-        use_hessp = not use_hess
-
-    elif use_hessp is None and use_hess is None:
-        use_hessp = method_info["uses_hessp"]
-        use_hess = method_info["uses_hess"]
-        if use_hessp and use_hess:
-            # If a method could use either hess or hessp, we default to using hessp
-            use_hess = False
-
-    return use_grad, use_hess, use_hessp
-
-
 def get_nearest_psd(A: np.ndarray) -> np.ndarray:
     """
     Compute the nearest positive semi-definite matrix to a given matrix.
diff --git a/pymc_extras/inference/laplace_approx/idata.py b/pymc_extras/inference/laplace_approx/idata.py
index 610b28b00..66f3a2bed 100644
--- a/pymc_extras/inference/laplace_approx/idata.py
+++ b/pymc_extras/inference/laplace_approx/idata.py
@@ -22,10 +22,15 @@ def make_default_labels(name: str, shape: tuple[int, ...]) -> list:
     return [list(range(dim)) for dim in shape]
 
 
-def make_unpacked_variable_names(names: list[str], model: pm.Model) -> list[str]:
+def make_unpacked_variable_names(
+    names: list[str], model: pm.Model, var_name_to_model_var: dict[str, str] | None = None
+) -> list[str]:
     coords = model.coords
     initial_point = model.initial_point()
 
+    if var_name_to_model_var is None:
+        var_name_to_model_var = {}
+
     value_to_dim = {
         value.name: model.named_vars_to_dims.get(model.values_to_rvs[value].name, None)
         for value in model.value_vars
@@ -37,6 +42,7 @@ def make_unpacked_variable_names(names: list[str], model: pm.Model) -> list[str]
 
     unpacked_variable_names = []
     for name in names:
+        name = var_name_to_model_var.get(name, name)
         shape = initial_point[name].shape
         if shape:
             dims = dims_dict.get(name)
@@ -258,6 +264,7 @@ def optimizer_result_to_dataset(
     method: minimize_method | Literal["basinhopping"],
     mu: RaveledVars | None = None,
     model: pm.Model | None = None,
+    var_name_to_model_var: dict[str, str] | None = None,
 ) -> xr.Dataset:
     """
     Convert an OptimizeResult object to an xarray Dataset object.
@@ -268,6 +275,9 @@ def optimizer_result_to_dataset(
         The result of the optimization process.
     method: minimize_method or "basinhopping"
         The optimization method used.
+    var_name_to_model_var: dict, optional
+        Mapping between variables in the optimization result and the model variable names. Used when auxiliary
+        variables were introduced, e.g. in DADVI.
 
     Returns
     -------
@@ -279,7 +289,9 @@ def optimizer_result_to_dataset(
 
     model = pm.modelcontext(model) if model is None else model
     variable_names, *_ = zip(*mu.point_map_info)
-    unpacked_variable_names = make_unpacked_variable_names(variable_names, model)
+    unpacked_variable_names = make_unpacked_variable_names(
+        variable_names, model, var_name_to_model_var
+    )
 
     data_vars = {}
 
@@ -368,6 +380,7 @@ def add_optimizer_result_to_inference_data(
     method: minimize_method | Literal["basinhopping"],
     mu: RaveledVars | None = None,
     model: pm.Model | None = None,
+    var_name_to_model_var: dict[str, str] | None = None,
 ) -> az.InferenceData:
     """
     Add the optimization result to an InferenceData object.
@@ -384,13 +397,18 @@ def add_optimizer_result_to_inference_data(
         The MAP estimate of the model parameters.
     model: Model, optional
         A PyMC model. If None, the model is taken from the current model context.
+    var_name_to_model_var: dict, optional
+        Mapping between variables in the optimization result and the model variable names. Used when auxiliary
+        variables were introduced, e.g. in DADVI.
 
     Returns
     -------
     idata: az.InferenceData
         The provided InferenceData, with the optimization results added to the "optimizer" group.
     """
-    dataset = optimizer_result_to_dataset(result, method=method, mu=mu, model=model)
+    dataset = optimizer_result_to_dataset(
+        result, method=method, mu=mu, model=model, var_name_to_model_var=var_name_to_model_var
+    )
     idata.add_groups({"optimizer_result": dataset})
 
     return idata
diff --git a/pymc_extras/inference/laplace_approx/scipy_interface.py b/pymc_extras/inference/laplace_approx/scipy_interface.py
index a7489be3e..99e2e491d 100644
--- a/pymc_extras/inference/laplace_approx/scipy_interface.py
+++ b/pymc_extras/inference/laplace_approx/scipy_interface.py
@@ -1,3 +1,5 @@
+import logging
+
 from collections.abc import Callable
 from importlib.util import find_spec
 from typing import Literal, get_args
@@ -6,6 +8,7 @@
 import pymc as pm
 import pytensor
 
+from better_optimize.constants import MINIMIZE_MODE_KWARGS
 from pymc import join_nonshared_inputs
 from pytensor import tensor as pt
 from pytensor.compile import Function
@@ -14,6 +17,39 @@
 GradientBackend = Literal["pytensor", "jax"]
 VALID_BACKENDS = get_args(GradientBackend)
 
+_log = logging.getLogger(__name__)
+
+
+def set_optimizer_function_defaults(
+    method: str, use_grad: bool | None, use_hess: bool | None, use_hessp: bool | None
+):
+    method_info = MINIMIZE_MODE_KWARGS[method].copy()
+
+    if use_hess and use_hessp:
+        _log.warning(
+            'Both "use_hess" and "use_hessp" are set to True, but scipy.optimize.minimize never uses both at the '
+            'same time. When possible "use_hessp" is preferred because its is computationally more efficient. '
+            'Setting "use_hess" to False.'
+        )
+        use_hess = False
+
+    use_grad = use_grad if use_grad is not None else method_info["uses_grad"]
+
+    if use_hessp is not None and use_hess is None:
+        use_hess = not use_hessp
+
+    elif use_hess is not None and use_hessp is None:
+        use_hessp = not use_hess
+
+    elif use_hessp is None and use_hess is None:
+        use_hessp = method_info["uses_hessp"]
+        use_hess = method_info["uses_hess"]
+        if use_hessp and use_hess:
+            # If a method could use either hess or hessp, we default to using hessp
+            use_hess = False
+
+    return use_grad, use_hess, use_hessp
+
 
 def _compile_grad_and_hess_to_jax(
     f_fused: Function, use_hess: bool, use_hessp: bool
@@ -144,12 +180,13 @@ def _compile_functions_for_scipy_optimize(
 def scipy_optimize_funcs_from_loss(
     loss: TensorVariable,
     inputs: list[TensorVariable],
-    initial_point_dict: dict[str, np.ndarray | float | int],
-    use_grad: bool,
-    use_hess: bool,
-    use_hessp: bool,
+    initial_point_dict: dict[str, np.ndarray | float | int] | None = None,
+    use_grad: bool | None = None,
+    use_hess: bool | None = None,
+    use_hessp: bool | None = None,
     gradient_backend: GradientBackend = "pytensor",
     compile_kwargs: dict | None = None,
+    inputs_are_flat: bool = False,
 ) -> tuple[Callable, ...]:
     """
     Compile loss functions for use with scipy.optimize.minimize.
@@ -206,9 +243,12 @@ def scipy_optimize_funcs_from_loss(
     if not isinstance(inputs, list):
         inputs = [inputs]
 
-    [loss], flat_input = join_nonshared_inputs(
-        point=initial_point_dict, outputs=[loss], inputs=inputs
-    )
+    if inputs_are_flat:
+        [flat_input] = inputs
+    else:
+        [loss], flat_input = join_nonshared_inputs(
+            point=initial_point_dict, outputs=[loss], inputs=inputs
+        )
 
     # If we use pytensor gradients, we will use the pytensor function wrapper that handles shared variables. When
     # computing jax gradients, we discard the function wrapper, so we can't handle shared variables --> rewrite them
diff --git a/tests/inference/dadvi/__init__.py b/tests/inference/dadvi/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/tests/inference/dadvi/test_dadvi.py b/tests/inference/dadvi/test_dadvi.py
new file mode 100644
index 000000000..52ffe4f37
--- /dev/null
+++ b/tests/inference/dadvi/test_dadvi.py
@@ -0,0 +1,137 @@
+import numpy as np
+import pymc as pm
+import pytest
+
+import pymc_extras as pmx
+
+from pymc_extras.inference.dadvi.dadvi import fit_dadvi
+
+
+@pytest.fixture(scope="session")
+def rng():
+    seed = sum(map(ord, "test_dadvi"))
+    return np.random.default_rng(seed)
+
+
+@pytest.mark.parametrize(
+    "mode, gradient_backend",
+    [(None, "pytensor"), ("NUMBA", "pytensor"), ("JAX", "jax"), ("JAX", "pytensor")],
+)
+def test_fit_dadvi_basic(mode, gradient_backend):
+    # Example from BDA3, section 4.1 (same as Laplace test)
+    y = np.array([2642, 3503, 4358], dtype=np.float64)
+    n = y.size
+    draws = 10000
+
+    with pm.Model() as m:
+        mu = pm.Flat("mu")
+        logsigma = pm.Flat("logsigma")
+
+        pm.Normal("y", mu=mu, sigma=pm.math.exp(logsigma), observed=y)
+
+        idata = pmx.fit(
+            method="dadvi",
+            optimizer_method="trust-ncg",
+            n_fixed_draws=draws,
+            n_draws=draws,
+            compile_kwargs={"mode": mode},
+            gradient_backend=gradient_backend,
+        )
+
+    assert idata.posterior["mu"].shape == (1, draws)
+    assert idata.posterior["logsigma"].shape == (1, draws)
+    assert idata.observed_data["y"].shape == (n,)
+
+    bda_map = [y.mean(), np.log(y.std())]
+
+    np.testing.assert_allclose(idata.posterior["mu"].mean(), bda_map[0], atol=1, rtol=1e-1)
+    np.testing.assert_allclose(idata.posterior["logsigma"].mean(), bda_map[1], atol=1, rtol=1e-1)
+
+
+def test_fit_dadvi_outside_model_context():
+    with pm.Model() as m:
+        mu = pm.Normal("mu", 0, 1)
+        sigma = pm.Exponential("sigma", 1)
+        y_hat = pm.Normal("y_hat", mu=mu, sigma=sigma, observed=np.random.normal(size=10))
+
+    idata = fit_dadvi(
+        model=m,
+        optimizer_method="L-BFGS-B",
+        use_grad=True,
+        progressbar=False,
+        n_draws=100,
+    )
+
+    assert hasattr(idata, "posterior")
+    assert hasattr(idata, "optimizer_result")
+    assert idata.posterior["mu"].shape == (1, 100)
+
+
+@pytest.mark.parametrize(
+    "include_transformed", [True, False], ids=["include_transformed", "no_transformed"]
+)
+def test_fit_dadvi_coords(include_transformed, rng):
+    coords = {"city": ["A", "B", "C"], "obs_idx": np.arange(100)}
+    with pm.Model(coords=coords) as model:
+        mu = pm.Normal("mu", mu=3, sigma=0.5, dims=["city"])
+        sigma = pm.Exponential("sigma", 1, dims=["city"])
+        obs = pm.Normal(
+            "obs",
+            mu=mu,
+            sigma=sigma,
+            observed=rng.normal(loc=3, scale=1.5, size=(100, 3)),
+            dims=["obs_idx", "city"],
+        )
+
+        idata = pmx.fit(
+            method="dadvi",
+            optimizer_method="trust-ncg",
+            n_draws=1000,
+            keep_untransformed=include_transformed,
+        )
+
+    np.testing.assert_allclose(
+        idata.posterior.mu.mean(dim=["chain", "draw"]).values, np.full((3,), 3), atol=0.5
+    )
+    np.testing.assert_allclose(
+        idata.posterior.sigma.mean(dim=["chain", "draw"]).values, np.full((3,), 1.5), atol=0.3
+    )
+
+    if include_transformed:
+        assert "sigma_log__" in idata.posterior
+        assert "city" in idata.posterior.coords
+
+
+def test_fit_dadvi_ragged_coords(rng):
+    coords = {"city": ["A", "B", "C"], "feature": [0, 1], "obs_idx": np.arange(100)}
+    with pm.Model(coords=coords) as ragged_dim_model:
+        X = pm.Data("X", np.ones((100, 2)), dims=["obs_idx", "feature"])
+        beta = pm.Normal(
+            "beta", mu=[[-100.0, 100.0], [-100.0, 100.0], [-100.0, 100.0]], dims=["city", "feature"]
+        )
+        mu = pm.Deterministic(
+            "mu", (X[:, None, :] * beta[None]).sum(axis=-1), dims=["obs_idx", "city"]
+        )
+        sigma = pm.HalfNormal("sigma", sigma=3.0, dims=["city"])
+
+        obs = pm.Normal(
+            "obs",
+            mu=mu,
+            sigma=sigma,
+            observed=rng.normal(loc=3, scale=1.5, size=(100, 3)),
+            dims=["obs_idx", "city"],
+        )
+
+        idata = fit_dadvi(
+            optimizer_method="trust-ncg",
+            use_grad=True,
+            use_hessp=True,
+        )
+
+    assert idata["posterior"].beta.shape[-2:] == (3, 2)
+    assert idata["posterior"].sigma.shape[-1:] == (3,)
+
+    # Check that everything got unraveled correctly -- feature 0 should be strictly negative, feature 1
+    # strictly positive
+    assert (idata["posterior"].beta.sel(feature=0).to_numpy() < 0).all()
+    assert (idata["posterior"].beta.sel(feature=1).to_numpy() > 0).all()
diff --git a/tests/inference/laplace_approx/test_find_map.py b/tests/inference/laplace_approx/test_find_map.py
index 3529f972d..22697e523 100644
--- a/tests/inference/laplace_approx/test_find_map.py
+++ b/tests/inference/laplace_approx/test_find_map.py
@@ -6,11 +6,11 @@
 from pymc_extras.inference.laplace_approx.find_map import (
     find_MAP,
     get_nearest_psd,
-    set_optimizer_function_defaults,
 )
 from pymc_extras.inference.laplace_approx.scipy_interface import (
     GradientBackend,
     scipy_optimize_funcs_from_loss,
+    set_optimizer_function_defaults,
 )
 
 

From e8c2a6058ca01bb803f51399c3d4f7d879a3e619 Mon Sep 17 00:00:00 2001
From: jessegrabowski <jessegrabowski@gmail.com>
Date: Thu, 18 Sep 2025 19:11:18 -0500
Subject: [PATCH 2/5] Rename `include_transformed` for consistency, and return
 uncontrained_posterior in a separate group

---
 pymc_extras/inference/dadvi/dadvi.py | 31 +++++++++++++++++++---------
 tests/inference/dadvi/test_dadvi.py  | 11 +++++++---
 2 files changed, 29 insertions(+), 13 deletions(-)

diff --git a/pymc_extras/inference/dadvi/dadvi.py b/pymc_extras/inference/dadvi/dadvi.py
index 899cb4472..4df01148b 100644
--- a/pymc_extras/inference/dadvi/dadvi.py
+++ b/pymc_extras/inference/dadvi/dadvi.py
@@ -33,7 +33,7 @@ def fit_dadvi(
     n_fixed_draws: int = 30,
     random_seed: RandomSeed = None,
     n_draws: int = 1000,
-    keep_untransformed: bool = False,
+    include_transformed: bool = False,
     optimizer_method: minimize_method = "trust-ncg",
     use_grad: bool | None = None,
     use_hessp: bool | None = None,
@@ -63,7 +63,7 @@ def fit_dadvi(
     n_draws: int
         The number of draws to return from the variational approximation.
 
-    keep_untransformed: bool
+    include_transformed: bool
         Whether or not to keep the unconstrained variables (such as logs of positive-constrained parameters) in the
         output.
 
@@ -166,9 +166,7 @@ def fit_dadvi(
     draws = opt_means + draws_raw * np.exp(opt_log_sds)
     draws_arviz = unstack_laplace_draws(draws, model, chains=1, draws=n_draws)
 
-    idata = az.InferenceData(
-        posterior=transform_draws(draws_arviz, model, keep_untransformed=keep_untransformed)
-    )
+    idata = dadvi_result_to_idata(draws_arviz, model, include_transformed=include_transformed)
 
     var_name_to_model_var = {f"{var_name}_mu": var_name for var_name in initial_point_dict.keys()}
     var_name_to_model_var.update(
@@ -251,10 +249,10 @@ def create_dadvi_graph(
     return var_params, objective
 
 
-def transform_draws(
+def dadvi_result_to_idata(
     unstacked_draws: xarray.Dataset,
     model: Model,
-    keep_untransformed: bool = False,
+    include_transformed: bool = False,
 ):
     """
     Transforms the unconstrained draws back into the constrained space.
@@ -270,7 +268,7 @@ def transform_draws(
     n_draws: int
         The number of draws to return from the variational approximation.
 
-    keep_untransformed: bool
+    include_transformed: bool
         Whether or not to keep the unconstrained variables in the output.
 
     Returns
@@ -281,7 +279,7 @@ def transform_draws(
 
     filtered_var_names = model.unobserved_value_vars
     vars_to_sample = list(
-        get_default_varnames(filtered_var_names, include_transformed=keep_untransformed)
+        get_default_varnames(filtered_var_names, include_transformed=include_transformed)
     )
     fn = pytensor.function(model.value_vars, vars_to_sample)
     point_func = PointFunc(fn)
@@ -296,4 +294,17 @@ def transform_draws(
         dims=dims,
     )
 
-    return transformed_result
+    constrained_names = [
+        x.name for x in get_default_varnames(model.unobserved_value_vars, include_transformed=False)
+    ]
+    all_varnames = [
+        x.name for x in get_default_varnames(model.unobserved_value_vars, include_transformed=True)
+    ]
+    unconstrained_names = set(all_varnames) - set(constrained_names)
+
+    idata = az.InferenceData(posterior=transformed_result[constrained_names])
+
+    if unconstrained_names and include_transformed:
+        idata["unconstrained_posterior"] = transformed_result[unconstrained_names]
+
+    return idata
diff --git a/tests/inference/dadvi/test_dadvi.py b/tests/inference/dadvi/test_dadvi.py
index 52ffe4f37..5b202c9f9 100644
--- a/tests/inference/dadvi/test_dadvi.py
+++ b/tests/inference/dadvi/test_dadvi.py
@@ -87,7 +87,7 @@ def test_fit_dadvi_coords(include_transformed, rng):
             method="dadvi",
             optimizer_method="trust-ncg",
             n_draws=1000,
-            keep_untransformed=include_transformed,
+            include_transformed=include_transformed,
         )
 
     np.testing.assert_allclose(
@@ -98,8 +98,13 @@ def test_fit_dadvi_coords(include_transformed, rng):
     )
 
     if include_transformed:
-        assert "sigma_log__" in idata.posterior
-        assert "city" in idata.posterior.coords
+        assert "unconstrained_posterior" in idata
+        assert "sigma_log__" in idata.unconstrained_posterior
+
+        # FIXME: The automatic coordinate inference used in MAP/Laplace doesn't work in DADVI yet, so city is not
+        #  propagated to the unconstrained_posterior group.
+        with pytest.raises(AssertionError):
+            assert "city" in idata.unconstrained_posterior.coords
 
 
 def test_fit_dadvi_ragged_coords(rng):

From b8164ff3fc547b67b7a078912d4ceef331d33b09 Mon Sep 17 00:00:00 2001
From: jessegrabowski <jessegrabowski@gmail.com>
Date: Thu, 18 Sep 2025 19:18:51 -0500
Subject: [PATCH 3/5] re-run dadvi notebook

---
 notebooks/deterministic_advi_example.ipynb | 2034 ++++++++++++++++----
 1 file changed, 1609 insertions(+), 425 deletions(-)

diff --git a/notebooks/deterministic_advi_example.ipynb b/notebooks/deterministic_advi_example.ipynb
index 0cf1d177c..87ca26645 100644
--- a/notebooks/deterministic_advi_example.ipynb
+++ b/notebooks/deterministic_advi_example.ipynb
@@ -18,62 +18,71 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "ea4459ce-54a4-4fd5-bafa-e5cd77f299c4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pymc as pm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
    "id": "b0f44abd-6fea-4f34-ab98-677b0858327a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Running on PyMC v5.25.1\n"
-     ]
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-19T00:20:59.326494Z",
+     "start_time": "2025-09-19T00:20:56.214089Z"
     }
-   ],
+   },
    "source": [
     "import arviz as az\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import pandas as pd\n",
-    "import xarray as xr\n",
     "\n",
     "import pymc as pm\n",
     "\n",
     "from pymc import HalfCauchy, Model, Normal, sample\n",
     "\n",
     "print(f\"Running on PyMC v{pm.__version__}\")"
-   ]
+   ],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on PyMC v5.25.1\n"
+     ]
+    }
+   ],
+   "execution_count": 1
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
    "id": "df7fd6b1-4961-444e-91a2-20f74654c8a1",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-19T00:20:59.332270Z",
+     "start_time": "2025-09-19T00:20:59.329301Z"
+    }
+   },
    "source": [
     "RANDOM_SEED = 8927\n",
     "rng = np.random.default_rng(RANDOM_SEED)\n",
     "\n",
     "%config InlineBackend.figure_format = 'retina'\n",
     "az.style.use(\"arviz-darkgrid\")"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 2
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
    "id": "d9da527a-baec-4a67-b6f5-d769282addbb",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-19T00:20:59.336315Z",
+     "start_time": "2025-09-19T00:20:59.334327Z"
+    }
+   },
    "source": [
     "size = 200\n",
     "true_intercept = 1\n",
@@ -86,43 +95,71 @@
     "y = true_regression_line + rng.normal(scale=0.5, size=size)\n",
     "\n",
     "data = pd.DataFrame({\"x\": x, \"y\": y})"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 3
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
    "id": "886e6283-e6b9-41c0-b8fb-21c696e1de5a",
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-19T00:20:59.479471Z",
+     "start_time": "2025-09-19T00:20:59.339148Z"
+    }
+   },
+   "source": [
+    "fig = plt.figure(figsize=(7, 7))\n",
+    "ax = fig.add_subplot(111, xlabel=\"x\", ylabel=\"y\", title=\"Generated data and underlying model\")\n",
+    "ax.plot(x, y, \"x\", label=\"sampled data\")\n",
+    "ax.plot(x, true_regression_line, label=\"true regression line\", lw=2.0)\n",
+    "plt.legend(loc=0);"
+   ],
    "outputs": [
     {
      "data": {
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       "text/plain": [
        "<Figure size 700x700 with 1 Axes>"
-      ]
+      ],
+      "image/png": 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"
      },
      "metadata": {
       "image/png": {
-       "height": 711,
-       "width": 711
+       "width": 711,
+       "height": 711
       }
      },
-     "output_type": "display_data"
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
     }
    ],
-   "source": [
-    "fig = plt.figure(figsize=(7, 7))\n",
-    "ax = fig.add_subplot(111, xlabel=\"x\", ylabel=\"y\", title=\"Generated data and underlying model\")\n",
-    "ax.plot(x, y, \"x\", label=\"sampled data\")\n",
-    "ax.plot(x, true_regression_line, label=\"true regression line\", lw=2.0)\n",
-    "plt.legend(loc=0);"
-   ]
+   "execution_count": 4
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
    "id": "d8186653-8967-4658-a52f-6440237bccdc",
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-19T00:21:02.208482Z",
+     "start_time": "2025-09-19T00:20:59.485775Z"
+    }
+   },
+   "source": [
+    "with Model() as model:  # model specifications in PyMC are wrapped in a with-statement\n",
+    "    # Define priors\n",
+    "    sigma = HalfCauchy(\"sigma\", beta=10)\n",
+    "    intercept = Normal(\"Intercept\", 0, sigma=20)\n",
+    "    slope = Normal(\"slope\", 0, sigma=20)\n",
+    "\n",
+    "    # Define likelihood\n",
+    "    likelihood = Normal(\"y\", mu=intercept + slope * x, sigma=sigma, observed=y)\n",
+    "\n",
+    "    # Inference!\n",
+    "    # draw 3000 posterior samples using NUTS sampling\n",
+    "    idata = sample(3000, random_seed=rng)"
+   ],
    "outputs": [
     {
      "name": "stderr",
@@ -135,63 +172,43 @@
     },
     {
      "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ca1af33594e84a2db519d729405704f2",
-       "version_major": 2,
-       "version_minor": 0
-      },
       "text/plain": [
        "Output()"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
       ],
-      "text/plain": []
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "cb8c6453693b42858342f593903c301e"
+      }
      },
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
     },
     {
      "data": {
+      "text/plain": [],
       "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\n"
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
       ]
      },
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Sampling 4 chains for 1_000 tune and 3_000 draw iterations (4_000 + 12_000 draws total) took 2 seconds.\n"
+      "Sampling 4 chains for 1_000 tune and 3_000 draw iterations (4_000 + 12_000 draws total) took 1 seconds.\n"
      ]
     }
    ],
-   "source": [
-    "with Model() as model:  # model specifications in PyMC are wrapped in a with-statement\n",
-    "    # Define priors\n",
-    "    sigma = HalfCauchy(\"sigma\", beta=10)\n",
-    "    intercept = Normal(\"Intercept\", 0, sigma=20)\n",
-    "    slope = Normal(\"slope\", 0, sigma=20)\n",
-    "\n",
-    "    # Define likelihood\n",
-    "    likelihood = Normal(\"y\", mu=intercept + slope * x, sigma=sigma, observed=y)\n",
-    "\n",
-    "    # Inference!\n",
-    "    # draw 3000 posterior samples using NUTS sampling\n",
-    "    idata = sample(3000)"
-   ]
+   "execution_count": 5
   },
   {
    "cell_type": "markdown",
@@ -203,77 +220,137 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
    "id": "b18297f4-6592-4afb-a1e3-364b81039e8d",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-19T00:21:03.033016Z",
+     "start_time": "2025-09-19T00:21:02.998421Z"
+    }
+   },
    "source": [
     "# This single import should be all that's needed\n",
     "from pymc_extras.inference import fit_dadvi"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 6
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
    "id": "dc6e3943-cd0d-4c66-a177-6e5f2a34c5a1",
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-19T00:21:04.496368Z",
+     "start_time": "2025-09-19T00:21:03.043764Z"
+    }
+   },
+   "source": [
+    "# We can fit DADVI now. It prints some progress of its convergence -- note we could change this\n",
+    "# to look different / remove it.\n",
+    "# Note that unlike regular ADVI, deterministic ADVI converges automatically. This is one of its key advantages.\n",
+    "with model:\n",
+    "    dadvi_res = fit_dadvi(random_seed=rng)"
+   ],
    "outputs": [
     {
      "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "498e10628ec243e7b595a5d05ad15a8e",
-       "version_major": 2,
-       "version_minor": 0
-      },
       "text/plain": [
        "Output()"
-      ]
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "cc60ba5dabb74b928eb5219c0604d725"
+      }
      },
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
     },
     {
      "data": {
+      "text/plain": [],
       "text/html": [
        "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Output()"
       ],
-      "text/plain": []
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "05849597991f45f69c3da3658da2f327"
+      }
      },
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
     },
     {
      "data": {
+      "text/plain": [],
       "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\n"
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
       ]
      },
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
     }
    ],
-   "source": [
-    "# We can fit DADVI now. It prints some progress of its convergence -- note we could change this\n",
-    "# to look different / remove it.\n",
-    "# Note that unlike regular ADVI, deterministic ADVI converges automatically. This is one of its key advantages.\n",
-    "with model:\n",
-    "    dadvi_res = fit_dadvi()"
-   ]
+   "execution_count": 7
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
    "id": "c47cb209-907b-4873-a3bf-39bff5578502",
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-19T00:21:04.525784Z",
+     "start_time": "2025-09-19T00:21:04.509661Z"
+    }
+   },
+   "source": [
+    "# DADVI returns draws like MCMC\n",
+    "dadvi_res"
+   ],
    "outputs": [
     {
      "data": {
+      "text/plain": [
+       "Inference data with groups:\n",
+       "\t> posterior\n",
+       "\t> optimizer_result\n",
+       "\t> observed_data"
+      ],
       "text/html": [
-       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "\n",
+       "            <div>\n",
+       "              <div class='xr-header'>\n",
+       "                <div class=\"xr-obj-type\">arviz.InferenceData</div>\n",
+       "              </div>\n",
+       "              <ul class=\"xr-sections group-sections\">\n",
+       "              \n",
+       "            <li class = \"xr-section-item\">\n",
+       "                  <input id=\"idata_posterior7c810585-77ad-4322-82b8-87ee9914f8eb\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
+       "                  <label for=\"idata_posterior7c810585-77ad-4322-82b8-87ee9914f8eb\" class = \"xr-section-summary\">posterior</label>\n",
+       "                  <div class=\"xr-section-inline-details\"></div>\n",
+       "                  <div class=\"xr-section-details\">\n",
+       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
+       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
        "<defs>\n",
        "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
        "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
@@ -293,76 +370,28 @@
        " */\n",
        "\n",
        ":root {\n",
-       "  --xr-font-color0: var(\n",
-       "    --jp-content-font-color0,\n",
-       "    var(--pst-color-text-base rgba(0, 0, 0, 1))\n",
-       "  );\n",
-       "  --xr-font-color2: var(\n",
-       "    --jp-content-font-color2,\n",
-       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))\n",
-       "  );\n",
-       "  --xr-font-color3: var(\n",
-       "    --jp-content-font-color3,\n",
-       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\n",
-       "  );\n",
-       "  --xr-border-color: var(\n",
-       "    --jp-border-color2,\n",
-       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
-       "  );\n",
-       "  --xr-disabled-color: var(\n",
-       "    --jp-layout-color3,\n",
-       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
-       "  );\n",
-       "  --xr-background-color: var(\n",
-       "    --jp-layout-color0,\n",
-       "    var(--pst-color-on-background, white)\n",
-       "  );\n",
-       "  --xr-background-color-row-even: var(\n",
-       "    --jp-layout-color1,\n",
-       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
-       "  );\n",
-       "  --xr-background-color-row-odd: var(\n",
-       "    --jp-layout-color2,\n",
-       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
-       "  );\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
        "}\n",
        "\n",
        "html[theme=\"dark\"],\n",
        "html[data-theme=\"dark\"],\n",
        "body[data-theme=\"dark\"],\n",
        "body.vscode-dark {\n",
-       "  --xr-font-color0: var(\n",
-       "    --jp-content-font-color0,\n",
-       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
-       "  );\n",
-       "  --xr-font-color2: var(\n",
-       "    --jp-content-font-color2,\n",
-       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
-       "  );\n",
-       "  --xr-font-color3: var(\n",
-       "    --jp-content-font-color3,\n",
-       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
-       "  );\n",
-       "  --xr-border-color: var(\n",
-       "    --jp-border-color2,\n",
-       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
-       "  );\n",
-       "  --xr-disabled-color: var(\n",
-       "    --jp-layout-color3,\n",
-       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
-       "  );\n",
-       "  --xr-background-color: var(\n",
-       "    --jp-layout-color0,\n",
-       "    var(--pst-color-on-background, #111111)\n",
-       "  );\n",
-       "  --xr-background-color-row-even: var(\n",
-       "    --jp-layout-color1,\n",
-       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
-       "  );\n",
-       "  --xr-background-color-row-odd: var(\n",
-       "    --jp-layout-color2,\n",
-       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
-       "  );\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1f1f1f;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
        "}\n",
        "\n",
        ".xr-wrap {\n",
@@ -418,7 +447,6 @@
        "\n",
        ".xr-section-item input + label {\n",
        "  color: var(--xr-disabled-color);\n",
-       "  border: 2px solid transparent !important;\n",
        "}\n",
        "\n",
        ".xr-section-item input:enabled + label {\n",
@@ -427,7 +455,7 @@
        "}\n",
        "\n",
        ".xr-section-item input:focus + label {\n",
-       "  border: 2px solid var(--xr-font-color0) !important;\n",
+       "  border: 2px solid var(--xr-font-color0);\n",
        "}\n",
        "\n",
        ".xr-section-item input:enabled + label:hover {\n",
@@ -559,9 +587,7 @@
        ".xr-var-item label,\n",
        ".xr-var-item > .xr-var-name span {\n",
        "  background-color: var(--xr-background-color-row-even);\n",
-       "  border-color: var(--xr-background-color-row-odd);\n",
        "  margin-bottom: 0;\n",
-       "  padding-top: 2px;\n",
        "}\n",
        "\n",
        ".xr-var-item > .xr-var-name:hover span {\n",
@@ -572,7 +598,6 @@
        ".xr-var-list > li:nth-child(odd) > label,\n",
        ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
        "  background-color: var(--xr-background-color-row-odd);\n",
-       "  border-color: var(--xr-background-color-row-even);\n",
        "}\n",
        "\n",
        ".xr-var-name {\n",
@@ -622,15 +647,8 @@
        ".xr-var-data,\n",
        ".xr-index-data {\n",
        "  display: none;\n",
-       "  border-top: 2px dotted var(--xr-background-color);\n",
-       "  padding-bottom: 20px !important;\n",
-       "  padding-top: 10px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in + label,\n",
-       ".xr-var-data-in + label,\n",
-       ".xr-index-data-in + label {\n",
-       "  padding: 0 1px;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
        "}\n",
        "\n",
        ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
@@ -643,12 +661,6 @@
        "  float: right;\n",
        "}\n",
        "\n",
-       ".xr-var-data > pre,\n",
-       ".xr-index-data > pre,\n",
-       ".xr-var-data > table > tbody > tr {\n",
-       "  background-color: transparent !important;\n",
-       "}\n",
-       "\n",
        ".xr-var-name span,\n",
        ".xr-var-data,\n",
        ".xr-index-name div,\n",
@@ -708,247 +720,1419 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
-       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
-       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
-       "  color: var(--xr-font-color0);\n",
-       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
-       "  stroke-width: 0.8px;\n",
-       "}\n",
        "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 32kB\n",
        "Dimensions:    (chain: 1, draw: 1000)\n",
        "Coordinates:\n",
        "  * chain      (chain) int64 8B 0\n",
        "  * draw       (draw) int64 8kB 0 1 2 3 4 5 6 7 ... 993 994 995 996 997 998 999\n",
        "Data variables:\n",
-       "    Intercept  (chain, draw) float64 8kB 1.035 1.124 1.073 ... 1.072 1.056 1.044\n",
-       "    slope      (chain, draw) float64 8kB 2.135 2.02 2.016 ... 1.916 2.137 1.978\n",
-       "    sigma      (chain, draw) float64 8kB 0.5326 0.5372 0.4899 ... 0.5494 0.5225\n",
+       "    Intercept  (chain, draw) float64 8kB 1.057 1.03 0.9942 ... 0.9848 0.9706\n",
+       "    slope      (chain, draw) float64 8kB 2.033 2.118 2.131 ... 2.141 2.087 1.996\n",
+       "    sigma      (chain, draw) float64 8kB 0.5354 0.5371 0.6019 ... 0.5204 0.536\n",
        "Attributes:\n",
-       "    created_at:                 2025-08-29T09:17:23.403627+00:00\n",
-       "    arviz_version:              0.22.0\n",
+       "    created_at:                 2025-09-19T00:21:04.484541+00:00\n",
+       "    arviz_version:              0.21.0\n",
        "    inference_library:          pymc\n",
-       "    inference_library_version:  5.25.1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-631fc9a1-c5a4-44c4-8b35-c38eb8d513f5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-631fc9a1-c5a4-44c4-8b35-c38eb8d513f5' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 1</li><li><span class='xr-has-index'>draw</span>: 1000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1439726c-811b-4211-ae7b-b13ee1f5bccd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1439726c-811b-4211-ae7b-b13ee1f5bccd' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-2e822d03-4a54-4895-9108-40b5c6e132e7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2e822d03-4a54-4895-9108-40b5c6e132e7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb083b4e-5642-48bd-a027-0387fba70b55' class='xr-var-data-in' type='checkbox'><label for='data-bb083b4e-5642-48bd-a027-0387fba70b55' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-0772daa3-eca1-45cf-8f82-9303fd9d7d41' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0772daa3-eca1-45cf-8f82-9303fd9d7d41' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1107e2c-c52d-498a-acb4-78883c5c4858' class='xr-var-data-in' type='checkbox'><label for='data-b1107e2c-c52d-498a-acb4-78883c5c4858' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999], shape=(1000,))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3f726f00-99bc-4945-98ee-e0645ef9f11d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3f726f00-99bc-4945-98ee-e0645ef9f11d' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>Intercept</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.035 1.124 1.073 ... 1.056 1.044</div><input id='attrs-6050140c-0e27-4f85-aea9-ed9f39313c7b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6050140c-0e27-4f85-aea9-ed9f39313c7b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a0fefd73-bdb7-4843-ad62-3af67a5642bc' class='xr-var-data-in' type='checkbox'><label for='data-a0fefd73-bdb7-4843-ad62-3af67a5642bc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.03450719, 1.12404185, 1.07284057, 1.0355449 , 1.05269621,\n",
-       "        1.06486973, 1.03567461, 1.01001655, 1.0779964 , 1.07547399,\n",
-       "        1.00593378, 1.06321878, 1.04291872, 1.03859156, 1.08286933,\n",
-       "        1.07476485, 1.10689774, 1.05456632, 1.0262287 , 1.12423367,\n",
-       "        1.03339705, 1.03681747, 1.12365433, 1.03800955, 1.04847361,\n",
-       "        1.13263132, 1.06365727, 1.07778219, 1.07465272, 1.05553961,\n",
-       "        1.04925698, 1.12508203, 1.04941021, 1.07083481, 1.01335218,\n",
-       "        1.03190349, 1.07838952, 1.04467789, 0.99704356, 1.12914412,\n",
-       "        0.98616493, 1.05983754, 1.12465586, 1.03059777, 1.11454044,\n",
-       "        0.93745207, 1.04271849, 1.04304819, 1.05811418, 1.05921293,\n",
-       "        1.01376329, 1.09078683, 1.06059874, 1.00874282, 1.06422887,\n",
-       "        1.03552499, 1.09279141, 1.04641005, 1.05898508, 1.06925862,\n",
-       "        1.02026127, 1.0885863 , 1.1083716 , 1.09426568, 1.10026869,\n",
-       "        1.02119611, 1.06798453, 1.05042329, 1.06575947, 1.04552637,\n",
-       "        1.05586102, 1.06004754, 1.07245808, 1.03031088, 1.11274971,\n",
-       "        1.0402724 , 1.07501044, 1.0756596 , 1.0889485 , 1.05687303,\n",
-       "        1.05226843, 1.06614733, 1.13440352, 1.03897371, 1.13661279,\n",
-       "        0.97957715, 1.04040987, 1.0036449 , 1.05483566, 1.05877806,\n",
-       "        1.06934829, 1.04022991, 1.0661489 , 1.00662534, 1.12294633,\n",
-       "        1.07419408, 1.02992842, 1.00412583, 1.04125683, 0.98389852,\n",
+       "    inference_library_version:  5.25.1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-1b07a44a-bba3-4b6f-8f79-2fa9aee3a0b2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1b07a44a-bba3-4b6f-8f79-2fa9aee3a0b2' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 1</li><li><span class='xr-has-index'>draw</span>: 1000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-98af69c1-8cfe-4fa2-9a08-1743dd70e2b6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-98af69c1-8cfe-4fa2-9a08-1743dd70e2b6' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-25b95e8e-77f5-41bf-8e61-2b9f36fc0206' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-25b95e8e-77f5-41bf-8e61-2b9f36fc0206' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc72cf9c-e442-45bc-aea6-2ddf451b6023' class='xr-var-data-in' type='checkbox'><label for='data-cc72cf9c-e442-45bc-aea6-2ddf451b6023' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-c02828d4-21c9-463b-9689-4b3ccbe55768' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c02828d4-21c9-463b-9689-4b3ccbe55768' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b25fb64d-0b8f-4017-84c3-0d0a34343c07' class='xr-var-data-in' type='checkbox'><label for='data-b25fb64d-0b8f-4017-84c3-0d0a34343c07' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999], shape=(1000,))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-915163ef-7f64-4295-8796-b162a0c967de' class='xr-section-summary-in' type='checkbox'  checked><label for='section-915163ef-7f64-4295-8796-b162a0c967de' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>Intercept</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.057 1.03 0.9942 ... 0.9848 0.9706</div><input id='attrs-5825c91f-556e-4659-98bf-6d945b2dc81f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5825c91f-556e-4659-98bf-6d945b2dc81f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44971443-934f-4b84-88a5-6892ec61781b' class='xr-var-data-in' type='checkbox'><label for='data-44971443-934f-4b84-88a5-6892ec61781b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.05723311, 1.03031721, 0.99419326, 1.09861771, 1.0042967 ,\n",
+       "        1.04474495, 1.04825675, 1.01048728, 1.09785671, 1.00612135,\n",
+       "        1.09324934, 1.01189271, 1.0107908 , 1.04148039, 1.07883476,\n",
+       "        1.06041335, 0.97855131, 1.09314927, 1.03970961, 1.12471352,\n",
+       "        1.07701199, 1.02557246, 1.00625267, 1.04207979, 1.03157385,\n",
+       "        1.08172679, 1.0381055 , 1.0169643 , 0.95016373, 0.96228983,\n",
+       "        1.03712554, 1.10706596, 1.07704334, 1.03567005, 1.06161746,\n",
+       "        1.05280669, 1.07170521, 0.9675774 , 1.0882704 , 1.02653109,\n",
+       "        1.06616209, 1.03140894, 1.02909274, 1.08633694, 1.06057293,\n",
+       "        1.08376077, 1.10438845, 1.06817767, 0.98142651, 1.04743106,\n",
+       "        1.00655258, 1.108425  , 1.11169893, 1.09629604, 1.02931376,\n",
+       "        0.97151511, 1.00761693, 1.05419808, 1.09568762, 1.05448199,\n",
+       "        1.06842904, 1.03495172, 1.03662442, 1.07708341, 0.99976349,\n",
+       "        1.07262502, 1.00281736, 1.03279895, 1.06276972, 1.06635411,\n",
+       "        1.09945655, 1.03770345, 1.05390427, 1.05892734, 1.13147637,\n",
+       "        1.00301651, 1.12963044, 1.13224593, 1.06245069, 1.04666137,\n",
+       "        1.02657232, 1.05878357, 1.04011373, 1.00544657, 1.00592955,\n",
+       "        0.98921166, 0.97541146, 1.01758853, 1.07800976, 0.99585365,\n",
+       "        1.01610549, 1.05263644, 1.05111165, 0.97123612, 1.02385376,\n",
+       "        1.05464764, 1.02356082, 1.08235252, 0.97814872, 1.07354631,\n",
        "...\n",
-       "        1.06026285, 1.05856002, 1.12645802, 1.10663868, 0.99962505,\n",
-       "        1.01264119, 0.99539589, 1.10675695, 1.07942247, 1.059351  ,\n",
-       "        1.0431628 , 1.00263697, 1.0100349 , 1.06836279, 1.06204536,\n",
-       "        0.98267112, 1.04619062, 1.03399779, 1.07933802, 1.02541312,\n",
-       "        1.05430098, 1.01414103, 1.0584926 , 1.09169297, 1.04353866,\n",
-       "        1.075776  , 0.99387987, 1.0520299 , 1.03210475, 1.08752915,\n",
-       "        1.03131456, 1.0650897 , 1.04549413, 1.03393103, 1.0197556 ,\n",
-       "        1.04313008, 1.02277172, 1.0320253 , 1.09684357, 1.05479382,\n",
-       "        1.02216497, 1.08027538, 1.07594051, 1.03228856, 1.0645997 ,\n",
-       "        1.04087824, 1.07186868, 1.0359763 , 1.02019948, 1.06027197,\n",
-       "        1.0588793 , 1.07008607, 1.05743038, 1.01390011, 1.05462943,\n",
-       "        1.13608908, 1.04652145, 1.0786749 , 1.01870285, 1.04566494,\n",
-       "        1.03842399, 1.06607466, 1.06282215, 1.04895395, 1.084762  ,\n",
-       "        1.03285704, 1.04833865, 1.0297054 , 1.0447197 , 0.96034714,\n",
-       "        1.10255616, 1.04554731, 1.00736384, 0.94247991, 0.97579409,\n",
-       "        1.03592156, 1.12877216, 0.98829111, 1.07300577, 1.05256632,\n",
-       "        1.06001004, 1.02358679, 1.11122867, 1.03175528, 1.10593716,\n",
-       "        1.05215054, 1.06375037, 1.06743093, 1.0762863 , 1.07555632,\n",
-       "        1.07424439, 1.03239951, 1.03692767, 1.01004056, 1.0211714 ,\n",
-       "        1.09189748, 1.03729933, 1.07150997, 1.05575355, 1.04397902]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>slope</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.135 2.02 2.016 ... 2.137 1.978</div><input id='attrs-2cc15363-9038-41fe-be0f-73fc94682bb5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2cc15363-9038-41fe-be0f-73fc94682bb5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e979fdd7-ad4f-426c-b8a9-776d29573457' class='xr-var-data-in' type='checkbox'><label for='data-e979fdd7-ad4f-426c-b8a9-776d29573457' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[2.13526056, 2.01976166, 2.01643312, 2.1100348 , 2.07716026,\n",
-       "        2.06160349, 2.06613744, 2.03665422, 2.09809492, 2.09254394,\n",
-       "        2.02756415, 1.96559854, 2.09336456, 2.09941884, 2.07120116,\n",
-       "        2.13126182, 2.02666477, 2.06201123, 2.05417933, 2.12346161,\n",
-       "        2.09077862, 2.0074942 , 2.12080933, 2.00242344, 2.06793194,\n",
-       "        2.06001931, 2.0466106 , 2.08614369, 1.97368227, 1.98172394,\n",
-       "        2.14971156, 2.10285983, 2.1481018 , 2.01721005, 2.13593258,\n",
-       "        2.04115995, 1.96189652, 2.04707556, 2.0781611 , 2.07677721,\n",
-       "        2.07627751, 1.97791313, 2.11992672, 2.12936483, 2.12374748,\n",
-       "        2.0466026 , 2.01615419, 2.05596055, 1.9777476 , 2.13762518,\n",
-       "        2.03398458, 2.04633336, 2.14269094, 2.10244966, 2.10752382,\n",
-       "        2.0693766 , 2.22017251, 2.13948308, 2.14241035, 2.02394195,\n",
-       "        2.10883631, 2.0551413 , 2.1365009 , 1.99529271, 2.14329751,\n",
-       "        2.07287906, 2.10762982, 2.15293469, 2.12383469, 2.0634459 ,\n",
-       "        1.95373318, 2.10923418, 2.07188133, 1.99761423, 1.96427444,\n",
-       "        2.04238656, 1.97135546, 2.07041228, 1.88843565, 2.11176987,\n",
-       "        1.99602479, 2.0804802 , 2.0478783 , 2.09637487, 2.07691669,\n",
-       "        2.15639889, 2.10513962, 2.09618181, 2.0727942 , 2.11354571,\n",
-       "        2.02491604, 2.07390872, 2.09198968, 2.02821292, 2.1144547 ,\n",
-       "        2.1213888 , 2.06974038, 2.13031491, 2.07850523, 2.07986512,\n",
+       "        0.98078902, 1.00924521, 1.00797322, 1.0257104 , 1.04553794,\n",
+       "        1.08065254, 1.01942054, 1.06336232, 1.07872642, 1.05516895,\n",
+       "        1.11743322, 1.00568329, 1.08816713, 1.03190688, 1.02626249,\n",
+       "        1.018009  , 1.11680049, 1.02822684, 1.11809922, 0.97459   ,\n",
+       "        0.96311543, 0.97397554, 1.09341642, 1.01186865, 1.10069711,\n",
+       "        1.08468441, 1.12265443, 1.03841151, 1.02813587, 1.08908073,\n",
+       "        1.01852045, 0.97156011, 1.07475292, 1.04298035, 1.06891097,\n",
+       "        1.00375869, 1.10342583, 1.07520738, 1.08080742, 1.10630105,\n",
+       "        1.08483653, 1.05937702, 1.04035931, 0.99795283, 1.10724953,\n",
+       "        1.07781647, 1.00630811, 1.05795125, 1.02232186, 1.05002289,\n",
+       "        1.04907609, 1.07529906, 0.97676905, 1.07360038, 1.05754507,\n",
+       "        1.10893484, 0.96772644, 0.97908831, 1.02366059, 1.03599467,\n",
+       "        1.10053454, 1.05630521, 1.08234168, 1.01176498, 0.98980971,\n",
+       "        1.0486603 , 1.06639293, 1.00051694, 0.99659325, 1.00755258,\n",
+       "        1.01037217, 0.9989211 , 1.07836688, 1.06486025, 1.04439838,\n",
+       "        0.99810521, 1.04705292, 1.05846189, 0.99873335, 1.08655046,\n",
+       "        0.99192223, 1.04981602, 0.98964622, 0.99246664, 1.0205237 ,\n",
+       "        1.06670967, 1.05782517, 1.01263406, 1.07748732, 1.18334639,\n",
+       "        1.00025012, 1.12937508, 0.98023992, 1.03200169, 1.0147056 ,\n",
+       "        1.06196186, 1.02419162, 1.02044223, 0.98478314, 0.97059803]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>slope</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.033 2.118 2.131 ... 2.087 1.996</div><input id='attrs-b7699213-65c5-44bc-aa33-14dd9d7fd8fb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b7699213-65c5-44bc-aa33-14dd9d7fd8fb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd53b6cf-93df-4ec0-b351-4df319f51118' class='xr-var-data-in' type='checkbox'><label for='data-fd53b6cf-93df-4ec0-b351-4df319f51118' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[2.03315421, 2.11762012, 2.13136252, 2.1343583 , 2.12868688,\n",
+       "        2.07940741, 2.09551785, 1.92889332, 2.14942941, 1.95745715,\n",
+       "        2.06920655, 2.12602595, 2.04230458, 2.00645585, 2.00943189,\n",
+       "        1.94389829, 2.05565699, 2.19666625, 2.08442738, 2.10840548,\n",
+       "        2.1026219 , 2.15566223, 1.97405333, 1.89724879, 2.21348108,\n",
+       "        1.97402423, 2.05602518, 2.02404634, 2.19449242, 2.09568697,\n",
+       "        1.96188989, 2.04535966, 2.09404814, 1.98128093, 2.08475728,\n",
+       "        1.99891495, 1.95418003, 2.02343056, 2.05063285, 2.00849415,\n",
+       "        2.02014295, 2.02675267, 2.05373757, 2.09589265, 2.15113676,\n",
+       "        2.10127873, 2.01537177, 2.11863456, 2.04194221, 1.91052061,\n",
+       "        2.06517845, 2.11702578, 1.98920505, 2.06215844, 2.02252048,\n",
+       "        2.07407778, 1.89762627, 1.95905273, 2.00802028, 2.10795857,\n",
+       "        2.14703134, 2.0506861 , 2.02990875, 2.01652309, 1.90170541,\n",
+       "        2.0629543 , 2.14093605, 2.05479507, 1.98830893, 1.88917177,\n",
+       "        2.00092873, 2.05413646, 2.16164352, 2.00522689, 2.19799114,\n",
+       "        2.13115181, 2.07184505, 2.16425584, 2.0204868 , 2.19236881,\n",
+       "        2.01523982, 2.1107692 , 2.0630956 , 1.92302009, 2.04801875,\n",
+       "        2.10523383, 1.95478982, 2.05384745, 2.1329188 , 2.06568097,\n",
+       "        1.99087528, 2.08647599, 2.12263602, 1.9671661 , 1.98105535,\n",
+       "        1.99319551, 2.02431089, 2.10607634, 1.9090718 , 2.06697983,\n",
        "...\n",
-       "        1.95377299, 2.07299154, 2.08773249, 2.0950276 , 2.0840157 ,\n",
-       "        1.94326163, 2.1146822 , 2.03226881, 2.0327619 , 2.15917823,\n",
-       "        2.1647793 , 2.0489871 , 2.01168585, 2.12423138, 2.07436678,\n",
-       "        2.05096594, 2.01863714, 1.99049235, 2.03861127, 2.05794492,\n",
-       "        2.05342403, 2.06229559, 2.02606089, 2.02588043, 1.97129773,\n",
-       "        1.9862503 , 2.05746853, 2.03718671, 2.05751782, 2.08399276,\n",
-       "        2.01749703, 2.0273369 , 2.08165982, 2.04587386, 2.06884539,\n",
-       "        2.11138419, 2.08389299, 2.0665134 , 2.0662659 , 2.0529737 ,\n",
-       "        2.06338812, 2.11736249, 2.02716203, 2.05943285, 2.10110448,\n",
-       "        2.04586508, 2.08758385, 2.11562952, 2.0112505 , 2.09746652,\n",
-       "        2.00545621, 2.07737075, 2.06264581, 2.13263472, 2.15148326,\n",
-       "        2.0327248 , 2.14187075, 1.95844653, 2.11834145, 2.0789889 ,\n",
-       "        2.12580091, 1.99683959, 2.07587761, 2.00148807, 2.06914397,\n",
-       "        2.0828802 , 2.05046188, 2.18535514, 2.10103676, 2.04384018,\n",
-       "        2.20641808, 2.03272998, 2.08264412, 2.05596795, 2.13429528,\n",
-       "        2.15763574, 1.99226268, 2.0615368 , 2.01405204, 2.11076401,\n",
-       "        1.97203144, 2.09147639, 2.11059479, 2.04237988, 2.00835348,\n",
-       "        2.00578911, 1.95496487, 2.01592049, 2.11643542, 1.99534615,\n",
-       "        2.09458572, 2.03907215, 2.11650492, 2.0667664 , 2.20412043,\n",
-       "        2.04051161, 2.12280236, 1.91616671, 2.13718065, 1.97779649]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sigma</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5326 0.5372 ... 0.5494 0.5225</div><input id='attrs-5ffecad8-2931-424c-a922-1556cc6db33c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5ffecad8-2931-424c-a922-1556cc6db33c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-944be671-3e0e-4c35-8d6b-781bf6dd1d6c' class='xr-var-data-in' type='checkbox'><label for='data-944be671-3e0e-4c35-8d6b-781bf6dd1d6c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.53264018, 0.53717447, 0.48992772, 0.52784995, 0.57495731,\n",
-       "        0.52021126, 0.48452951, 0.50852162, 0.53890904, 0.55340005,\n",
-       "        0.52866686, 0.51638479, 0.51678022, 0.53452048, 0.52253251,\n",
-       "        0.50468992, 0.54709953, 0.49655893, 0.51929181, 0.54087612,\n",
-       "        0.55549906, 0.4960475 , 0.50259419, 0.54051639, 0.51313389,\n",
-       "        0.48945666, 0.51928298, 0.50201345, 0.5711675 , 0.51704249,\n",
-       "        0.55083564, 0.56031261, 0.52319737, 0.5397157 , 0.52000357,\n",
-       "        0.53081549, 0.51794995, 0.50646896, 0.54164087, 0.50659058,\n",
-       "        0.54561014, 0.5044941 , 0.50907795, 0.51322754, 0.5389222 ,\n",
-       "        0.48278161, 0.51162806, 0.53620641, 0.54856009, 0.52705693,\n",
-       "        0.49550617, 0.5152358 , 0.51043223, 0.53360809, 0.53592136,\n",
-       "        0.5111027 , 0.53302632, 0.54810645, 0.52316052, 0.56580837,\n",
-       "        0.52733979, 0.51659383, 0.49696807, 0.5283922 , 0.50891086,\n",
-       "        0.51962843, 0.51180083, 0.48832967, 0.50527832, 0.50534059,\n",
-       "        0.54619304, 0.56943168, 0.49405664, 0.49086313, 0.50816539,\n",
-       "        0.5092055 , 0.53231752, 0.50111379, 0.54712904, 0.59172865,\n",
-       "        0.50459637, 0.53398471, 0.52215424, 0.54201509, 0.48829487,\n",
-       "        0.51921649, 0.49641592, 0.50928266, 0.5468533 , 0.50367815,\n",
-       "        0.55179881, 0.52593306, 0.51342638, 0.52577341, 0.52186543,\n",
-       "        0.56506259, 0.54184535, 0.53798149, 0.53190068, 0.52866095,\n",
+       "        2.00547836, 2.08450257, 2.0438764 , 2.10633475, 2.02975313,\n",
+       "        2.07202845, 1.97771735, 2.05168945, 2.10736569, 2.12449045,\n",
+       "        2.17176092, 2.05739714, 2.0164946 , 2.06391102, 2.11749067,\n",
+       "        2.10892876, 2.09390239, 1.98565164, 1.97083059, 2.13802   ,\n",
+       "        2.10006488, 2.01079707, 1.91425221, 2.10891756, 2.07452043,\n",
+       "        2.10461242, 2.10518927, 2.07708394, 2.00841043, 2.16003104,\n",
+       "        2.11829058, 2.0312556 , 2.1346152 , 1.96487141, 2.15624259,\n",
+       "        2.1266542 , 2.20568005, 2.12797119, 2.05705014, 2.09695241,\n",
+       "        2.09813743, 2.06482407, 2.06445941, 1.91297298, 2.06233144,\n",
+       "        2.09568078, 1.93755209, 2.11054518, 2.04185908, 2.08123088,\n",
+       "        2.06587409, 2.07888291, 1.98416424, 2.00925529, 2.12437241,\n",
+       "        2.01097038, 1.99553615, 2.09620417, 2.00564125, 2.03243128,\n",
+       "        1.94516255, 2.0916737 , 1.96060593, 1.98316074, 1.99517973,\n",
+       "        2.0550546 , 2.01973532, 2.04259465, 2.06887132, 2.18244129,\n",
+       "        2.12059436, 1.93895808, 2.14754144, 2.07633072, 2.15870011,\n",
+       "        2.03151989, 2.00145191, 2.18240109, 2.03124974, 2.17227685,\n",
+       "        2.06262809, 2.1866167 , 1.9635248 , 2.12375725, 2.053284  ,\n",
+       "        1.8919346 , 2.0403468 , 1.98530256, 2.00239054, 2.12003776,\n",
+       "        2.06184222, 1.99397958, 2.07496653, 1.97031856, 2.13927111,\n",
+       "        1.97835036, 2.02993538, 2.14129986, 2.08673999, 1.99617992]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sigma</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5354 0.5371 ... 0.5204 0.536</div><input id='attrs-c2bc10ec-1e48-42bb-9080-be2e1943355f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c2bc10ec-1e48-42bb-9080-be2e1943355f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2cc4d0ac-8e6c-415b-9854-29749902f0f8' class='xr-var-data-in' type='checkbox'><label for='data-2cc4d0ac-8e6c-415b-9854-29749902f0f8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.53538687, 0.53705607, 0.60192189, 0.47293035, 0.49627654,\n",
+       "        0.50128562, 0.51851404, 0.53873479, 0.48959782, 0.4859061 ,\n",
+       "        0.5146206 , 0.53192827, 0.4856638 , 0.54424523, 0.55738329,\n",
+       "        0.50819869, 0.54394699, 0.49609721, 0.51699148, 0.51566523,\n",
+       "        0.52655586, 0.55982098, 0.50381287, 0.51533378, 0.53080524,\n",
+       "        0.56230567, 0.51845593, 0.53033995, 0.53503343, 0.52898486,\n",
+       "        0.52043494, 0.50685351, 0.51108169, 0.51487933, 0.53904317,\n",
+       "        0.51157728, 0.55538331, 0.52832862, 0.5114554 , 0.50744298,\n",
+       "        0.51973134, 0.55449489, 0.52335918, 0.55509929, 0.54361456,\n",
+       "        0.53617292, 0.49321556, 0.55363792, 0.57507736, 0.5294352 ,\n",
+       "        0.58097525, 0.51265519, 0.54651359, 0.5399647 , 0.52554721,\n",
+       "        0.51234071, 0.51997523, 0.52612003, 0.51619858, 0.54709371,\n",
+       "        0.51623731, 0.49383536, 0.52002779, 0.48515855, 0.50222015,\n",
+       "        0.54083896, 0.53836374, 0.50727579, 0.49898146, 0.48828083,\n",
+       "        0.55042486, 0.48077163, 0.53235148, 0.52731091, 0.51862977,\n",
+       "        0.53272642, 0.50478634, 0.53388299, 0.53730587, 0.52623149,\n",
+       "        0.4917704 , 0.52365433, 0.55037273, 0.50788402, 0.49528912,\n",
+       "        0.51417018, 0.51413072, 0.56403217, 0.51626313, 0.50639116,\n",
+       "        0.55120615, 0.53863661, 0.56605703, 0.55791578, 0.52411904,\n",
+       "        0.49481171, 0.52481317, 0.49773872, 0.52548946, 0.52730686,\n",
        "...\n",
-       "        0.51840877, 0.53424211, 0.53230351, 0.51840357, 0.53568988,\n",
-       "        0.51861031, 0.53998692, 0.50997164, 0.50886542, 0.55812584,\n",
-       "        0.50072572, 0.5091212 , 0.48889386, 0.53539297, 0.51115525,\n",
-       "        0.54935576, 0.54626145, 0.52735292, 0.56348777, 0.51979597,\n",
-       "        0.50125276, 0.51590024, 0.51041533, 0.52272456, 0.54969642,\n",
-       "        0.50673748, 0.53936026, 0.52537917, 0.53152831, 0.53690814,\n",
-       "        0.51956512, 0.52349571, 0.55828896, 0.56220242, 0.51103659,\n",
-       "        0.52519377, 0.51913478, 0.48173689, 0.56643878, 0.51146205,\n",
-       "        0.52543889, 0.51829738, 0.53251095, 0.51040147, 0.55282101,\n",
-       "        0.48898372, 0.5455761 , 0.50603169, 0.52392182, 0.48820922,\n",
-       "        0.52766853, 0.50774293, 0.55526201, 0.52760508, 0.50884203,\n",
-       "        0.4889245 , 0.49749765, 0.4768087 , 0.49304827, 0.5094859 ,\n",
-       "        0.50554459, 0.50485576, 0.50819975, 0.48156455, 0.52926563,\n",
-       "        0.52540975, 0.51211321, 0.51647577, 0.51201661, 0.51002989,\n",
-       "        0.55418525, 0.52488632, 0.52798765, 0.50050277, 0.51918048,\n",
-       "        0.53881021, 0.53565841, 0.51765   , 0.52228744, 0.53795119,\n",
-       "        0.53609387, 0.53075469, 0.5208383 , 0.50786692, 0.54616517,\n",
-       "        0.51306281, 0.51950628, 0.52768231, 0.51758285, 0.56975647,\n",
-       "        0.56948705, 0.54578575, 0.55377635, 0.53860837, 0.53324766,\n",
-       "        0.51397341, 0.5115702 , 0.51110905, 0.54938327, 0.52246268]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d86ab683-8998-4613-b464-3b0dab04fc85' class='xr-section-summary-in' type='checkbox'  ><label for='section-d86ab683-8998-4613-b464-3b0dab04fc85' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-3973cc8b-8bc8-4ec2-a1ec-2aee80be60f0' class='xr-index-data-in' type='checkbox'/><label for='index-3973cc8b-8bc8-4ec2-a1ec-2aee80be60f0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-ea22dfa5-b42d-4bd9-9211-1fe65dfa4bcb' class='xr-index-data-in' type='checkbox'/><label for='index-ea22dfa5-b42d-4bd9-9211-1fe65dfa4bcb' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
+       "        0.51752077, 0.54537095, 0.56005137, 0.54593799, 0.52839311,\n",
+       "        0.5517956 , 0.51681172, 0.5330983 , 0.53226183, 0.50165661,\n",
+       "        0.56858252, 0.52152544, 0.5142764 , 0.55885737, 0.53392226,\n",
+       "        0.5537442 , 0.52934733, 0.54255378, 0.53706011, 0.51613198,\n",
+       "        0.53757385, 0.52245076, 0.53241243, 0.53939333, 0.49279537,\n",
+       "        0.54161957, 0.5644447 , 0.52635163, 0.49286626, 0.53681984,\n",
+       "        0.54095796, 0.52037639, 0.519913  , 0.55361435, 0.48078786,\n",
+       "        0.53877879, 0.54681739, 0.5097145 , 0.55284174, 0.5151017 ,\n",
+       "        0.52959616, 0.5323533 , 0.54379667, 0.54869223, 0.49902168,\n",
+       "        0.57282823, 0.49678557, 0.59317784, 0.54048666, 0.5401771 ,\n",
+       "        0.50315541, 0.51445431, 0.51411969, 0.51826423, 0.52635913,\n",
+       "        0.52621297, 0.57285722, 0.52144175, 0.5109235 , 0.52599011,\n",
+       "        0.53909253, 0.47605014, 0.50544876, 0.50969557, 0.52481057,\n",
+       "        0.51813551, 0.56668928, 0.56133456, 0.51383342, 0.5325521 ,\n",
+       "        0.49584928, 0.51977494, 0.54642194, 0.51098693, 0.55574529,\n",
+       "        0.52216666, 0.55299101, 0.51396769, 0.53873658, 0.49632394,\n",
+       "        0.54952654, 0.53403243, 0.53585154, 0.51205674, 0.55112952,\n",
+       "        0.57342751, 0.54137763, 0.56482489, 0.50915302, 0.56138349,\n",
+       "        0.5429772 , 0.50107353, 0.5202206 , 0.53160259, 0.51035586,\n",
+       "        0.51568567, 0.5238199 , 0.53006717, 0.52035696, 0.53598543]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-54006231-f617-4a8e-9e86-86bbb226e827' class='xr-section-summary-in' type='checkbox'  ><label for='section-54006231-f617-4a8e-9e86-86bbb226e827' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-17f31c16-ee63-46b1-b6d8-2b2a2f0090e4' class='xr-index-data-in' type='checkbox'/><label for='index-17f31c16-ee63-46b1-b6d8-2b2a2f0090e4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-900edb27-165a-4168-8403-8706cbeb9388' class='xr-index-data-in' type='checkbox'/><label for='index-900edb27-165a-4168-8403-8706cbeb9388' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
        "       ...\n",
        "       990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
-       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-173af013-b1fd-49f9-8400-bc11447bc2e2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-173af013-b1fd-49f9-8400-bc11447bc2e2' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2025-08-29T09:17:23.403627+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.22.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.25.1</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.Dataset> Size: 32kB\n",
-       "Dimensions:    (chain: 1, draw: 1000)\n",
-       "Coordinates:\n",
-       "  * chain      (chain) int64 8B 0\n",
-       "  * draw       (draw) int64 8kB 0 1 2 3 4 5 6 7 ... 993 994 995 996 997 998 999\n",
-       "Data variables:\n",
-       "    Intercept  (chain, draw) float64 8kB 1.035 1.124 1.073 ... 1.072 1.056 1.044\n",
-       "    slope      (chain, draw) float64 8kB 2.135 2.02 2.016 ... 1.916 2.137 1.978\n",
-       "    sigma      (chain, draw) float64 8kB 0.5326 0.5372 0.4899 ... 0.5494 0.5225\n",
-       "Attributes:\n",
-       "    created_at:                 2025-08-29T09:17:23.403627+00:00\n",
-       "    arviz_version:              0.22.0\n",
-       "    inference_library:          pymc\n",
-       "    inference_library_version:  5.25.1"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# DADVI returns draws like MCMC\n",
-    "dadvi_res"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "2d911a25-5970-4c30-bd2e-21aa0fa74217",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/s_/l9wd4yls3mv1f4kkkz1xhhmm0000gn/T/ipykernel_84113/2317749157.py:18: UserWarning: The figure layout has changed to tight\n",
-      "  f.tight_layout()\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 800x800 with 3 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 790,
-       "width": 790
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import seaborn as sns\n",
-    "\n",
-    "f, ax = plt.subplots(3, 1)\n",
-    "\n",
-    "sns.kdeplot(idata.posterior.Intercept.values.reshape(-1), ax=ax[0], label=\"MCMC\")\n",
-    "sns.kdeplot(dadvi_res.Intercept.values.reshape(-1), ax=ax[0], label=\"DADVI\")\n",
-    "\n",
-    "sns.kdeplot(idata.posterior.slope.values.reshape(-1), ax=ax[1], label=\"MCMC\")\n",
-    "sns.kdeplot(dadvi_res.slope.values.reshape(-1), ax=ax[1], label=\"DADVI\")\n",
-    "\n",
-    "sns.kdeplot(idata.posterior.sigma.values.reshape(-1), ax=ax[2], label=\"MCMC\")\n",
-    "sns.kdeplot(dadvi_res.sigma.values.reshape(-1), ax=ax[2], label=\"DADVI\")\n",
-    "\n",
-    "for cur_ax in ax:\n",
-    "    cur_ax.legend()\n",
-    "\n",
-    "f.set_size_inches(8, 8)\n",
-    "f.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "853f8ee0-5730-47c8-ba6b-d2fed86677b4",
-   "metadata": {},
-   "source": [
-    "We can see that the posteriors don't align perfectly. DADVI does a pretty good job matching the means, but underestimates the variance somewhat. However, often DADVI is much faster than MCMC, and so this is a tradeoff that can make sense.\n",
-    "\n",
-    "Note that DADVI as explained in the [paper](https://jmlr.org/papers/volume25/23-1015/23-1015.pdf) has further tools to make variances more accurate, which we plan to add to PyMC later on. For now, its key advantage is that it converges automatically and reliably, removing one headache from ADVI."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "78df242e-a266-4dda-8ca4-43b85bc92327",
-   "metadata": {},
+       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-08c8e2d8-2e70-4221-87f3-8d0286772868' class='xr-section-summary-in' type='checkbox'  checked><label for='section-08c8e2d8-2e70-4221-87f3-8d0286772868' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2025-09-19T00:21:04.484541+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.21.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.25.1</dd></dl></div></li></ul></div></div><br></div>\n",
+       "                      </ul>\n",
+       "                  </div>\n",
+       "            </li>\n",
+       "            \n",
+       "            <li class = \"xr-section-item\">\n",
+       "                  <input id=\"idata_optimizer_resultb8cbcba9-cb91-474a-bdc7-ad7aa67f79c8\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
+       "                  <label for=\"idata_optimizer_resultb8cbcba9-cb91-474a-bdc7-ad7aa67f79c8\" class = \"xr-section-summary\">optimizer_result</label>\n",
+       "                  <div class=\"xr-section-inline-details\"></div>\n",
+       "                  <div class=\"xr-section-details\">\n",
+       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
+       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
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+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
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+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1f1f1f;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: \"►\";\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: \"▼\";\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 593B\n",
+       "Dimensions:    (variables: 6)\n",
+       "Coordinates:\n",
+       "  * variables  (variables) &lt;U11 264B &#x27;sigma_log__&#x27; &#x27;Intercept&#x27; ... &#x27;slope&#x27;\n",
+       "Data variables:\n",
+       "    x          (variables) float64 48B -0.6438 1.043 2.057 -3.072 -3.155 -2.591\n",
+       "    fun        float64 8B 173.9\n",
+       "    success    bool 1B True\n",
+       "    message    &lt;U37 148B &#x27;Optimization terminated successfully.&#x27;\n",
+       "    jac        (variables) float64 48B -9.823e-12 8.792e-12 ... 9.037e-10\n",
+       "    nit        int64 8B 20\n",
+       "    nfev       int64 8B 21\n",
+       "    njev       int64 8B 20\n",
+       "    status     int64 8B 0\n",
+       "    nhev       int64 8B 67\n",
+       "    method     &lt;U9 36B &#x27;trust-ncg&#x27;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-b76bc3bb-1f87-4f46-9620-2d4427c16414' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b76bc3bb-1f87-4f46-9620-2d4427c16414' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>variables</span>: 6</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d93160ab-5183-4743-9969-c4ffda5b4278' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d93160ab-5183-4743-9969-c4ffda5b4278' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>variables</span></div><div class='xr-var-dims'>(variables)</div><div class='xr-var-dtype'>&lt;U11</div><div class='xr-var-preview xr-preview'>&#x27;sigma_log__&#x27; ... &#x27;slope&#x27;</div><input id='attrs-026f1e88-9a51-466c-946e-01c4da29356c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-026f1e88-9a51-466c-946e-01c4da29356c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef6a73b7-b5c8-491c-aa75-d03fe17a8641' class='xr-var-data-in' type='checkbox'><label for='data-ef6a73b7-b5c8-491c-aa75-d03fe17a8641' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;sigma_log__&#x27;, &#x27;Intercept&#x27;, &#x27;slope&#x27;, &#x27;sigma_log__&#x27;, &#x27;Intercept&#x27;,\n",
+       "       &#x27;slope&#x27;], dtype=&#x27;&lt;U11&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-542c13ed-be89-47fd-8456-34d61c4f2800' class='xr-section-summary-in' type='checkbox'  checked><label for='section-542c13ed-be89-47fd-8456-34d61c4f2800' class='xr-section-summary' >Data variables: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>x</span></div><div class='xr-var-dims'>(variables)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.6438 1.043 ... -3.155 -2.591</div><input id='attrs-8ceae28c-b7d6-4178-a90a-797c18673b5c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8ceae28c-b7d6-4178-a90a-797c18673b5c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0030742b-2587-46ce-a7c3-dc38c29a3390' class='xr-var-data-in' type='checkbox'><label for='data-0030742b-2587-46ce-a7c3-dc38c29a3390' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-0.64377059,  1.04324719,  2.0566852 , -3.07171593, -3.15452966,\n",
+       "       -2.59112931])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fun</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>173.9</div><input id='attrs-c289a554-554a-4371-8b6a-9a981e95c6b6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c289a554-554a-4371-8b6a-9a981e95c6b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f38c1b4-221d-41ed-abc3-489468869e7b' class='xr-var-data-in' type='checkbox'><label for='data-9f38c1b4-221d-41ed-abc3-489468869e7b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(173.8947817)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>success</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>True</div><input id='attrs-1520e93e-8c01-473b-93d2-af5a68930d89' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1520e93e-8c01-473b-93d2-af5a68930d89' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-19ebc1e8-3024-4f77-bee4-78020326ebb4' class='xr-var-data-in' type='checkbox'><label for='data-19ebc1e8-3024-4f77-bee4-78020326ebb4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(True)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>message</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U37</div><div class='xr-var-preview xr-preview'>&#x27;Optimization terminated success...</div><input id='attrs-bb2801a7-5466-49de-b6c9-cd5e6543abac' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bb2801a7-5466-49de-b6c9-cd5e6543abac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b5a6cc75-021e-4cf9-b885-5584352b70e2' class='xr-var-data-in' type='checkbox'><label for='data-b5a6cc75-021e-4cf9-b885-5584352b70e2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;Optimization terminated successfully.&#x27;, dtype=&#x27;&lt;U37&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>jac</span></div><div class='xr-var-dims'>(variables)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.823e-12 8.792e-12 ... 9.037e-10</div><input id='attrs-26770f8e-c954-4caf-9029-898d1731c846' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-26770f8e-c954-4caf-9029-898d1731c846' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af0d97e0-c53a-4077-ac36-03c9a15887f7' class='xr-var-data-in' type='checkbox'><label for='data-af0d97e0-c53a-4077-ac36-03c9a15887f7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-9.82325332e-12,  8.79207818e-12,  9.01390074e-13,  1.41242995e-09,\n",
+       "        9.40430400e-10,  9.03730424e-10])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nit</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>20</div><input id='attrs-9cf44d5f-7c12-41b5-891c-b53255c13585' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9cf44d5f-7c12-41b5-891c-b53255c13585' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9b20da8-0591-41ed-9deb-443087b8cab7' class='xr-var-data-in' type='checkbox'><label for='data-d9b20da8-0591-41ed-9deb-443087b8cab7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(20)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nfev</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>21</div><input id='attrs-f22662a6-714c-47e8-95f4-fd1e91f86f1a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f22662a6-714c-47e8-95f4-fd1e91f86f1a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3cd1aa93-1601-4f9e-8588-e7e349ae4b3c' class='xr-var-data-in' type='checkbox'><label for='data-3cd1aa93-1601-4f9e-8588-e7e349ae4b3c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(21)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>njev</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>20</div><input id='attrs-920ad684-a6b3-469e-844e-d00ced76c56c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-920ad684-a6b3-469e-844e-d00ced76c56c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-110540a2-d7ac-4e8e-ba5b-36df94947cc1' class='xr-var-data-in' type='checkbox'><label for='data-110540a2-d7ac-4e8e-ba5b-36df94947cc1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(20)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>status</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-feef84ad-b9fd-4e74-9f57-68d0727788c7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-feef84ad-b9fd-4e74-9f57-68d0727788c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ab7b719-76fe-4714-bb32-3bb192851486' class='xr-var-data-in' type='checkbox'><label for='data-1ab7b719-76fe-4714-bb32-3bb192851486' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nhev</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>67</div><input id='attrs-e042e45e-2dd4-4dc5-91df-b2ad13c223c2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e042e45e-2dd4-4dc5-91df-b2ad13c223c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3fbb3a0a-5ed1-4330-b2f0-ae3d588b2641' class='xr-var-data-in' type='checkbox'><label for='data-3fbb3a0a-5ed1-4330-b2f0-ae3d588b2641' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(67)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>method</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U9</div><div class='xr-var-preview xr-preview'>&#x27;trust-ncg&#x27;</div><input id='attrs-a2ee7cf4-754f-4d9d-b0e7-87a3ba305fcc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a2ee7cf4-754f-4d9d-b0e7-87a3ba305fcc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-962fd35e-ccf3-4737-af5c-f93c1f78eafd' class='xr-var-data-in' type='checkbox'><label for='data-962fd35e-ccf3-4737-af5c-f93c1f78eafd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;trust-ncg&#x27;, dtype=&#x27;&lt;U9&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-24d5cc60-68a9-4cef-ae1b-6fddf2e4f0ee' class='xr-section-summary-in' type='checkbox'  ><label for='section-24d5cc60-68a9-4cef-ae1b-6fddf2e4f0ee' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>variables</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-f504270d-0d88-4f88-8377-a0ae290a85f2' class='xr-index-data-in' type='checkbox'/><label for='index-f504270d-0d88-4f88-8377-a0ae290a85f2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;sigma_log__&#x27;, &#x27;Intercept&#x27;, &#x27;slope&#x27;, &#x27;sigma_log__&#x27;, &#x27;Intercept&#x27;,\n",
+       "       &#x27;slope&#x27;],\n",
+       "      dtype=&#x27;object&#x27;, name=&#x27;variables&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ea712467-822f-4e7a-95bd-449f8bbf7c61' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ea712467-822f-4e7a-95bd-449f8bbf7c61' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div><br></div>\n",
+       "                      </ul>\n",
+       "                  </div>\n",
+       "            </li>\n",
+       "            \n",
+       "            <li class = \"xr-section-item\">\n",
+       "                  <input id=\"idata_observed_data3eaee904-3c25-498b-80f1-c35f52f5852d\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
+       "                  <label for=\"idata_observed_data3eaee904-3c25-498b-80f1-c35f52f5852d\" class = \"xr-section-summary\">observed_data</label>\n",
+       "                  <div class=\"xr-section-inline-details\"></div>\n",
+       "                  <div class=\"xr-section-details\">\n",
+       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
+       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1f1f1f;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: \"►\";\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: \"▼\";\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 3kB\n",
+       "Dimensions:  (y_dim_0: 200)\n",
+       "Coordinates:\n",
+       "  * y_dim_0  (y_dim_0) int64 2kB 0 1 2 3 4 5 6 7 ... 193 194 195 196 197 198 199\n",
+       "Data variables:\n",
+       "    y        (y_dim_0) float64 2kB 0.8887 1.673 0.7346 ... 3.651 2.462 2.891\n",
+       "Attributes:\n",
+       "    created_at:                 2025-09-19T00:21:04.494392+00:00\n",
+       "    arviz_version:              0.21.0\n",
+       "    inference_library:          pymc\n",
+       "    inference_library_version:  5.25.1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-f0b44fdf-d2b2-4a5d-b5d2-3357492a0eee' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f0b44fdf-d2b2-4a5d-b5d2-3357492a0eee' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>y_dim_0</span>: 200</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a13af936-c93b-4658-8ebd-4ae47cde26b6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a13af936-c93b-4658-8ebd-4ae47cde26b6' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y_dim_0</span></div><div class='xr-var-dims'>(y_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 195 196 197 198 199</div><input id='attrs-a91ae917-61d2-4c53-b46b-e54a5df782e1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a91ae917-61d2-4c53-b46b-e54a5df782e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e4f125aa-cd37-4f04-9d53-49a4c9291d2b' class='xr-var-data-in' type='checkbox'><label for='data-e4f125aa-cd37-4f04-9d53-49a4c9291d2b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,\n",
+       "        14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,\n",
+       "        28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,\n",
+       "        42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,\n",
+       "        56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,\n",
+       "        70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,\n",
+       "        84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,\n",
+       "        98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,\n",
+       "       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,\n",
+       "       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,\n",
+       "       140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,\n",
+       "       154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,\n",
+       "       168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,\n",
+       "       182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n",
+       "       196, 197, 198, 199])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bf0d089f-8e92-4fd5-9197-a3128e172490' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bf0d089f-8e92-4fd5-9197-a3128e172490' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>(y_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.8887 1.673 0.7346 ... 2.462 2.891</div><input id='attrs-660a0edb-8381-40c0-b7e6-2e9c7be3c8cf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-660a0edb-8381-40c0-b7e6-2e9c7be3c8cf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-437126c6-9099-422b-a29c-4fd95bc15fd4' class='xr-var-data-in' type='checkbox'><label for='data-437126c6-9099-422b-a29c-4fd95bc15fd4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.88867889, 1.67287962, 0.73461549, 1.36427536, 1.61289151,\n",
+       "       1.34392329, 1.03457986, 0.14782779, 0.92679183, 1.08457198,\n",
+       "       1.41822214, 1.16719733, 1.83000115, 1.5303612 , 0.92691374,\n",
+       "       1.91809781, 0.04712464, 1.31206193, 0.62012933, 1.84138961,\n",
+       "       1.45737559, 0.68005868, 1.17199294, 1.42576861, 0.26352765,\n",
+       "       1.41993441, 1.5000207 , 0.7197194 , 1.03204991, 1.74935449,\n",
+       "       0.98538177, 1.57327582, 1.51295293, 1.42074585, 0.96379935,\n",
+       "       0.93695167, 1.63479082, 2.01746536, 1.63031561, 0.77359109,\n",
+       "       0.88762221, 1.2181282 , 1.33973506, 1.44613147, 2.18824801,\n",
+       "       1.00585879, 1.26032578, 1.65762709, 1.5735179 , 2.17035368,\n",
+       "       1.5355243 , 0.93864488, 2.12576952, 1.67955663, 1.41967073,\n",
+       "       1.98393871, 2.03176464, 1.59618759, 0.98875271, 0.83004952,\n",
+       "       2.27214915, 1.81114054, 1.75790297, 1.50211682, 1.91732599,\n",
+       "       2.1365886 , 1.88423397, 2.06709914, 1.68909309, 2.8770839 ,\n",
+       "       1.76424643, 1.65234965, 2.03040157, 2.25873184, 1.63436932,\n",
+       "       1.44769762, 2.89373077, 2.26151965, 1.84929131, 1.53060477,\n",
+       "       1.99019732, 2.71573338, 1.4817264 , 0.95014488, 3.03312817,\n",
+       "       2.17718978, 1.54880425, 1.73213817, 2.20918205, 1.7004837 ,\n",
+       "       1.98317215, 0.91867518, 1.39840345, 3.07329404, 2.23201672,\n",
+       "       1.21536284, 2.88734548, 2.66055334, 2.12322219, 2.23065674,\n",
+       "       1.74525773, 1.97086847, 2.2535223 , 1.85623878, 2.4655761 ,\n",
+       "       2.02985718, 2.21139507, 2.23319192, 2.07893308, 1.55266056,\n",
+       "       1.9118171 , 2.07575979, 2.09438882, 3.49913929, 1.78898539,\n",
+       "       1.90404799, 2.65627086, 2.01838704, 2.74422255, 1.40176298,\n",
+       "       2.27762831, 1.92978129, 1.76448705, 2.76863545, 1.7830749 ,\n",
+       "       2.51107452, 1.64204917, 2.63340853, 2.32224479, 1.62849378,\n",
+       "       1.61096237, 2.2793419 , 2.41551231, 2.53226728, 3.51040655,\n",
+       "       3.03874978, 2.82069033, 1.69737014, 2.73694649, 2.58898597,\n",
+       "       3.76855678, 3.08076894, 2.77612306, 2.82992448, 2.05648878,\n",
+       "       3.01524286, 2.13559878, 2.74484603, 2.30475056, 2.6305342 ,\n",
+       "       2.09947429, 2.38540896, 2.62350214, 1.97143787, 3.65865798,\n",
+       "       2.07627304, 3.20404184, 2.7677549 , 2.74908382, 2.88809819,\n",
+       "       2.52438735, 2.98842508, 2.29156375, 2.41168459, 2.29779209,\n",
+       "       3.49230896, 2.84141413, 2.67130276, 2.45400705, 2.84590046,\n",
+       "       2.43524544, 2.04363743, 2.87286412, 2.92723372, 3.11961804,\n",
+       "       2.78171521, 3.46544384, 3.7590828 , 1.98325753, 3.5810146 ,\n",
+       "       2.60191172, 1.92935363, 3.27379328, 3.51201423, 3.20897203,\n",
+       "       3.81670233, 1.72774886, 1.88367311, 2.28634922, 2.9885757 ,\n",
+       "       2.50065363, 2.39363059, 3.59814785, 3.56031293, 3.19388115,\n",
+       "       2.77479922, 4.72124481, 3.65092008, 2.46235632, 2.89145879])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7b3dcdec-3bb3-4554-9bce-217053171874' class='xr-section-summary-in' type='checkbox'  ><label for='section-7b3dcdec-3bb3-4554-9bce-217053171874' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>y_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-87e2d0af-71b8-46e7-bb16-d0d785616d78' class='xr-index-data-in' type='checkbox'/><label for='index-87e2d0af-71b8-46e7-bb16-d0d785616d78' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
+       "       ...\n",
+       "       190, 191, 192, 193, 194, 195, 196, 197, 198, 199],\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;y_dim_0&#x27;, length=200))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-41644363-832c-4f85-a0af-530f63735661' class='xr-section-summary-in' type='checkbox'  checked><label for='section-41644363-832c-4f85-a0af-530f63735661' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2025-09-19T00:21:04.494392+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.21.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.25.1</dd></dl></div></li></ul></div></div><br></div>\n",
+       "                      </ul>\n",
+       "                  </div>\n",
+       "            </li>\n",
+       "            \n",
+       "              </ul>\n",
+       "            </div>\n",
+       "            <style> /* CSS stylesheet for displaying InferenceData objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-sections.group-sections {\n",
+       "  grid-template-columns: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       ".xr-wrap{width:700px!important;} </style>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 8
+  },
+  {
+   "cell_type": "code",
+   "id": "2d911a25-5970-4c30-bd2e-21aa0fa74217",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-19T00:21:04.744779Z",
+     "start_time": "2025-09-19T00:21:04.534848Z"
+    }
+   },
+   "source": [
+    "f, ax = plt.subplots(3, 1, figsize=(8, 8), layout=\"constrained\")\n",
+    "var_names = [\"Intercept\", \"slope\", \"sigma\"]\n",
+    "\n",
+    "for axis, name in zip(f.axes, var_names):\n",
+    "    az.plot_kde(\n",
+    "        idata.posterior[name].values.ravel(),\n",
+    "        ax=axis,\n",
+    "        plot_kwargs=dict(label=\"MCMC\", color=\"tab:blue\"),\n",
+    "    )\n",
+    "    az.plot_kde(\n",
+    "        dadvi_res.posterior[name].values.ravel(),\n",
+    "        ax=axis,\n",
+    "        plot_kwargs=dict(label=\"DADVI\", color=\"tab:orange\"),\n",
+    "    )\n",
+    "    axis.set_title(name)\n",
+    "    axis.legend()\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 800x800 with 3 Axes>"
+      ],
+      "image/png": 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+     },
+     "metadata": {
+      "image/png": {
+       "width": 811,
+       "height": 811
+      }
+     },
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 9
+  },
+  {
+   "cell_type": "markdown",
+   "id": "853f8ee0-5730-47c8-ba6b-d2fed86677b4",
+   "metadata": {},
+   "source": [
+    "We can see that the posteriors don't align perfectly. DADVI does a pretty good job matching the means, but underestimates the variance somewhat. However, often DADVI is much faster than MCMC, and so this is a tradeoff that can make sense.\n",
+    "\n",
+    "Note that DADVI as explained in the [paper](https://jmlr.org/papers/volume25/23-1015/23-1015.pdf) has further tools to make variances more accurate, which we plan to add to PyMC later on. For now, its key advantage is that it converges automatically and reliably, removing one headache from ADVI."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "78df242e-a266-4dda-8ca4-43b85bc92327",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-19T00:21:04.751950Z",
+     "start_time": "2025-09-19T00:21:04.750487Z"
+    }
+   },
+   "source": [],
    "outputs": [],
-   "source": []
+   "execution_count": null
   }
  ],
  "metadata": {

From b8d4048a23a890802265633c2b016c0e27d7dd59 Mon Sep 17 00:00:00 2001
From: jessegrabowski <jessegrabowski@gmail.com>
Date: Thu, 18 Sep 2025 19:33:24 -0500
Subject: [PATCH 4/5] Allow basinhopping when fitting DADVI

---
 pymc_extras/inference/dadvi/dadvi.py | 73 ++++++++++++++++++++++------
 tests/inference/dadvi/test_dadvi.py  | 39 +++++++++++++++
 2 files changed, 96 insertions(+), 16 deletions(-)

diff --git a/pymc_extras/inference/dadvi/dadvi.py b/pymc_extras/inference/dadvi/dadvi.py
index 4df01148b..bc81ce1f2 100644
--- a/pymc_extras/inference/dadvi/dadvi.py
+++ b/pymc_extras/inference/dadvi/dadvi.py
@@ -5,7 +5,7 @@
 import pytensor.tensor as pt
 import xarray
 
-from better_optimize import minimize
+from better_optimize import basinhopping, minimize
 from better_optimize.constants import minimize_method
 from pymc import DictToArrayBijection, Model, join_nonshared_inputs
 from pymc.backends.arviz import (
@@ -31,7 +31,6 @@
 def fit_dadvi(
     model: Model | None = None,
     n_fixed_draws: int = 30,
-    random_seed: RandomSeed = None,
     n_draws: int = 1000,
     include_transformed: bool = False,
     optimizer_method: minimize_method = "trust-ncg",
@@ -40,7 +39,9 @@ def fit_dadvi(
     use_hess: bool | None = None,
     gradient_backend: str = "pytensor",
     compile_kwargs: dict | None = None,
-    **minimize_kwargs,
+    random_seed: RandomSeed = None,
+    progressbar: bool = True,
+    **optimizer_kwargs,
 ) -> az.InferenceData:
     """
     Does inference using Deterministic ADVI (Automatic Differentiation Variational Inference), DADVI for short.
@@ -79,10 +80,6 @@ def fit_dadvi(
     compile_kwargs: dict, optional
         Additional keyword arguments to pass to `pytensor.function`
 
-    minimize_kwargs:
-        Additional keyword arguments to pass to the ``scipy.optimize.minimize`` function. See the documentation of
-        that function for details.
-
     use_grad: bool, optional
         If True, pass the gradient function to `scipy.optimize.minimize` (where it is referred to as `jac`).
 
@@ -93,6 +90,13 @@ def fit_dadvi(
         If True, pass the hessian to `scipy.optimize.minimize`. Note that this is generally not recommended since its
         computation can be slow and memory-intensive if there are many parameters.
 
+    progressbar: bool
+        Whether or not to show a progress bar during optimization. Default is True.
+
+    optimizer_kwargs:
+        Additional keyword arguments to pass to the ``scipy.optimize.minimize`` function. See the documentation of
+        that function for details.
+
     Returns
     -------
     :class:`~arviz.InferenceData`
@@ -105,6 +109,16 @@ def fit_dadvi(
     """
 
     model = pymc.modelcontext(model) if model is None else model
+    do_basinhopping = optimizer_method == "basinhopping"
+    minimizer_kwargs = optimizer_kwargs.pop("minimizer_kwargs", {})
+
+    if do_basinhopping:
+        # For a nice API, we let the user set method="basinhopping", but if we're doing basinhopping we still need
+        # another method for the inner optimizer. This will be set in the minimizer_kwargs, but also needs a default
+        # if one isn't provided.
+
+        optimizer_method = minimizer_kwargs.pop("method", "L-BFGS-B")
+        minimizer_kwargs["method"] = optimizer_method
 
     initial_point_dict = model.initial_point()
     initial_point = DictToArrayBijection.map(initial_point_dict)
@@ -145,14 +159,34 @@ def fit_dadvi(
     )
 
     dadvi_initial_point = DictToArrayBijection.map(dadvi_initial_point)
-
-    result = minimize(
-        f=f_fused,
-        x0=dadvi_initial_point.data,
-        method=optimizer_method,
-        hessp=f_hessp,
-        **minimize_kwargs,
-    )
+    args = optimizer_kwargs.pop("args", ())
+
+    if do_basinhopping:
+        if "args" not in minimizer_kwargs:
+            minimizer_kwargs["args"] = args
+        if "hessp" not in minimizer_kwargs:
+            minimizer_kwargs["hessp"] = f_hessp
+        if "method" not in minimizer_kwargs:
+            minimizer_kwargs["method"] = optimizer_method
+
+        result = basinhopping(
+            func=f_fused,
+            x0=dadvi_initial_point.data,
+            progressbar=progressbar,
+            minimizer_kwargs=minimizer_kwargs,
+            **optimizer_kwargs,
+        )
+
+    else:
+        result = minimize(
+            f=f_fused,
+            x0=dadvi_initial_point.data,
+            args=args,
+            method=optimizer_method,
+            hessp=f_hessp,
+            progressbar=progressbar,
+            **optimizer_kwargs,
+        )
 
     raveled_optimized = RaveledVars(result.x, dadvi_initial_point.point_map_info)
 
@@ -166,7 +200,9 @@ def fit_dadvi(
     draws = opt_means + draws_raw * np.exp(opt_log_sds)
     draws_arviz = unstack_laplace_draws(draws, model, chains=1, draws=n_draws)
 
-    idata = dadvi_result_to_idata(draws_arviz, model, include_transformed=include_transformed)
+    idata = dadvi_result_to_idata(
+        draws_arviz, model, include_transformed=include_transformed, progressbar=progressbar
+    )
 
     var_name_to_model_var = {f"{var_name}_mu": var_name for var_name in initial_point_dict.keys()}
     var_name_to_model_var.update(
@@ -253,6 +289,7 @@ def dadvi_result_to_idata(
     unstacked_draws: xarray.Dataset,
     model: Model,
     include_transformed: bool = False,
+    progressbar: bool = True,
 ):
     """
     Transforms the unconstrained draws back into the constrained space.
@@ -271,6 +308,9 @@ def dadvi_result_to_idata(
     include_transformed: bool
         Whether or not to keep the unconstrained variables in the output.
 
+    progressbar: bool
+        Whether or not to show a progress bar during the transformation. Default is True.
+
     Returns
     -------
     :class:`~arviz.InferenceData`
@@ -292,6 +332,7 @@ def dadvi_result_to_idata(
         output_var_names=[x.name for x in vars_to_sample],
         coords=coords,
         dims=dims,
+        progressbar=progressbar,
     )
 
     constrained_names = [
diff --git a/tests/inference/dadvi/test_dadvi.py b/tests/inference/dadvi/test_dadvi.py
index 5b202c9f9..1463db519 100644
--- a/tests/inference/dadvi/test_dadvi.py
+++ b/tests/inference/dadvi/test_dadvi.py
@@ -140,3 +140,42 @@ def test_fit_dadvi_ragged_coords(rng):
     # strictly positive
     assert (idata["posterior"].beta.sel(feature=0).to_numpy() < 0).all()
     assert (idata["posterior"].beta.sel(feature=1).to_numpy() > 0).all()
+
+
+@pytest.mark.parametrize(
+    "method, use_grad, use_hess, use_hessp",
+    [
+        ("Newton-CG", True, True, False),
+        ("Newton-CG", True, False, True),
+    ],
+)
+def test_dadvi_basinhopping(method, use_grad, use_hess, use_hessp, rng):
+    pytest.importorskip("jax")
+
+    with pm.Model() as m:
+        mu = pm.Normal("mu")
+        sigma = pm.Exponential("sigma", 1)
+        pm.Normal("y_hat", mu=mu, sigma=sigma, observed=rng.normal(loc=3, scale=1.5, size=10))
+
+        idata = fit_dadvi(
+            optimizer_method="basinhopping",
+            use_grad=use_grad,
+            use_hess=use_hess,
+            use_hessp=use_hessp,
+            progressbar=False,
+            include_transformed=True,
+            minimizer_kwargs=dict(method=method),
+            niter=1,
+            n_draws=100,
+        )
+
+    assert hasattr(idata, "posterior")
+    assert hasattr(idata, "unconstrained_posterior")
+
+    posterior = idata.posterior
+    unconstrained_posterior = idata.unconstrained_posterior
+    assert "mu" in posterior
+    assert posterior["mu"].shape == (1, 100)
+
+    assert "sigma_log__" in unconstrained_posterior
+    assert unconstrained_posterior["sigma_log__"].shape == (1, 100)

From 427dfb85b938559b8d4cd2e541b8903f5fe1857a Mon Sep 17 00:00:00 2001
From: jessegrabowski <jessegrabowski@gmail.com>
Date: Tue, 23 Sep 2025 07:51:37 -0500
Subject: [PATCH 5/5] Respond to feedback

---
 pymc_extras/inference/dadvi/dadvi.py          | 2 +-
 pymc_extras/inference/laplace_approx/idata.py | 2 +-
 tests/inference/dadvi/test_dadvi.py           | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/pymc_extras/inference/dadvi/dadvi.py b/pymc_extras/inference/dadvi/dadvi.py
index bc81ce1f2..04c3a24d9 100644
--- a/pymc_extras/inference/dadvi/dadvi.py
+++ b/pymc_extras/inference/dadvi/dadvi.py
@@ -341,7 +341,7 @@ def dadvi_result_to_idata(
     all_varnames = [
         x.name for x in get_default_varnames(model.unobserved_value_vars, include_transformed=True)
     ]
-    unconstrained_names = set(all_varnames) - set(constrained_names)
+    unconstrained_names = sorted(set(all_varnames) - set(constrained_names))
 
     idata = az.InferenceData(posterior=transformed_result[constrained_names])
 
diff --git a/pymc_extras/inference/laplace_approx/idata.py b/pymc_extras/inference/laplace_approx/idata.py
index 66f3a2bed..086177cda 100644
--- a/pymc_extras/inference/laplace_approx/idata.py
+++ b/pymc_extras/inference/laplace_approx/idata.py
@@ -115,7 +115,7 @@ def map_results_to_inference_data(
         x.name for x in get_default_varnames(model.unobserved_value_vars, include_transformed=True)
     ]
 
-    unconstrained_names = set(all_varnames) - set(constrained_names)
+    unconstrained_names = sorted(set(all_varnames) - set(constrained_names))
 
     idata = az.from_dict(
         posterior={
diff --git a/tests/inference/dadvi/test_dadvi.py b/tests/inference/dadvi/test_dadvi.py
index 1463db519..93a54132d 100644
--- a/tests/inference/dadvi/test_dadvi.py
+++ b/tests/inference/dadvi/test_dadvi.py
@@ -15,7 +15,7 @@ def rng():
 
 @pytest.mark.parametrize(
     "mode, gradient_backend",
-    [(None, "pytensor"), ("NUMBA", "pytensor"), ("JAX", "jax"), ("JAX", "pytensor")],
+    [(None, "pytensor")],
 )
 def test_fit_dadvi_basic(mode, gradient_backend):
     # Example from BDA3, section 4.1 (same as Laplace test)