From e94495e5d7e8251cfba23f071dd58dd95a587c41 Mon Sep 17 00:00:00 2001
From: Ben Zickel <ben@vesttoo.com>
Date: Sun, 19 May 2024 12:34:30 +0300
Subject: [PATCH 01/24] Added Stable distribution with unsafe log-probability
 calculation.

---
 pyro/distributions/__init__.py             |  2 +
 pyro/distributions/stable_with_log_prob.py | 95 ++++++++++++++++++++++
 2 files changed, 97 insertions(+)
 create mode 100644 pyro/distributions/stable_with_log_prob.py

diff --git a/pyro/distributions/__init__.py b/pyro/distributions/__init__.py
index 6ba7ef0be4..a43852334a 100644
--- a/pyro/distributions/__init__.py
+++ b/pyro/distributions/__init__.py
@@ -120,6 +120,7 @@
 from pyro.distributions.softlaplace import SoftLaplace
 from pyro.distributions.spanning_tree import SpanningTree
 from pyro.distributions.stable import Stable
+from pyro.distributions.stable_with_log_prob import StableWithLogProb
 from pyro.distributions.torch import __all__ as torch_dists
 from pyro.distributions.torch_distribution import (
     ExpandedDistribution,
@@ -234,6 +235,7 @@
     "SoftLaplace",
     "SpanningTree",
     "Stable",
+    "StableWithLogProb",
     "StudentT",
     "TorchDistribution",
     "TransformModule",
diff --git a/pyro/distributions/stable_with_log_prob.py b/pyro/distributions/stable_with_log_prob.py
new file mode 100644
index 0000000000..58d0cb61b7
--- /dev/null
+++ b/pyro/distributions/stable_with_log_prob.py
@@ -0,0 +1,95 @@
+import torch
+import math
+
+from functools import partial
+
+from scipy.special import roots_legendre
+
+from .stable import Stable
+
+def set_integrator(num_points):
+    global integrate
+    roots, weights = roots_legendre(num_points)
+    roots = torch.Tensor(roots).double()
+    weights = torch.Tensor(weights).double()
+    log_weights = weights.log()
+    half_roots = roots * 0.5
+    def integrate(fn, domain):
+        sl = [slice(None)] + (len(domain.shape) - 1) * [None]
+        half_roots_sl = half_roots[sl]
+        value = domain[0] * (0.5 - half_roots_sl) + domain[1] * (0.5 + half_roots_sl)
+        return torch.logsumexp(fn(value) + log_weights[sl], dim=0) + ((domain[1] - domain[0]) / 2).log()
+
+
+set_integrator(num_points=501)
+
+
+class StableWithLogProb(Stable):
+    def log_prob(self, value):
+        # Undo shift and scale
+        value = (value - self.loc) / self.scale
+
+        # Use double precision math
+        alpha = self.stability.double()
+        beta = self.skew.double()
+        value = value.double()
+
+        # Optionally convert from Nolan's parametrization S^0 where samples depend
+        # continuously on (alpha,beta), allowing interpolation around the hole at
+        # alpha=1.
+        if self.coords == "S0":
+            value = value + beta * (math.pi / 2 * alpha).tan()
+        elif self.coords != "S":
+            raise ValueError("Unknown coords: {}".format(self.coords))
+
+        # Integration is not valid for very small values
+        value = torch.where(value > 0, value.clamp(min=0.01), value.clamp(max=-0.01))
+
+        log_prob = _unsafe_stable_log_prob(alpha, beta, value)
+
+        return log_prob - self.scale.log()
+
+
+def _unsafe_stable_log_prob(alpha, beta, Z):
+    # Calculate log-probability of Z given V. This will fail if alpha is close to 1
+    # or if Z is close to 0
+    ha = math.pi / 2 * alpha
+    b = beta * ha.tan()
+    atan_b = b.atan()
+    u_zero = -alpha.reciprocal() * atan_b
+    
+    # If sample should be negative calculate with flipped beta and flipped value
+    flip_beta_x = Z < 0
+    beta = torch.where(flip_beta_x, -beta, beta)
+    u_zero = torch.where(flip_beta_x, -u_zero, u_zero)
+    Z = torch.where(flip_beta_x, -Z, Z)
+
+    # Set integration domwin
+    domain = torch.stack((u_zero, 0.5 * math.pi * u_zero.new_ones(u_zero.shape)), dim=0)
+
+    integrand = partial(_unsafe_stable_given_uniform_log_prob, alpha=alpha, beta=beta, Z=Z)
+    
+    return integrate(integrand, domain) - math.log(math.pi)
+
+
+def _unsafe_stable_given_uniform_log_prob(V, alpha, beta, Z):
+    # Calculate log-probability of Z given V. This will fail if alpha is close to 1
+    # or if Z is close to 0
+    inv_alpha = alpha.reciprocal()
+    half_pi = math.pi / 2
+    eps = torch.finfo(V.dtype).eps
+    # make V belong to the open interval (-pi/2, pi/2)
+    V = V.clamp(min=2 * eps - half_pi, max=half_pi - 2 * eps)
+    ha = half_pi * alpha
+    b = beta * ha.tan()
+    atan_b = b.atan()
+    
+    # +/- `ha` term to keep the precision of alpha * (V + half_pi) when V ~ -half_pi
+    v = atan_b - ha + alpha * (V + half_pi)
+    W = ( ( v.sin() / Z /
+           (atan_b.cos() * V.cos()).pow(inv_alpha)
+          ).pow(alpha / (1 - alpha))
+          * (v - V).cos().clamp(min=eps) 
+        )
+
+    return -W + (alpha * W / Z / (alpha - 1)).abs().log()
\ No newline at end of file

From 665a092ebf1a3a5def527c6d67724beb50a1284b Mon Sep 17 00:00:00 2001
From: Ben Zickel <ben@vesttoo.com>
Date: Sun, 19 May 2024 13:23:34 +0300
Subject: [PATCH 02/24] Make Stable distribution log-probability calculation
 safe at values near zero.

---
 pyro/distributions/stable_with_log_prob.py | 44 +++++++++++++++-------
 1 file changed, 31 insertions(+), 13 deletions(-)

diff --git a/pyro/distributions/stable_with_log_prob.py b/pyro/distributions/stable_with_log_prob.py
index 58d0cb61b7..b6a338ec5b 100644
--- a/pyro/distributions/stable_with_log_prob.py
+++ b/pyro/distributions/stable_with_log_prob.py
@@ -5,7 +5,7 @@
 
 from scipy.special import roots_legendre
 
-from .stable import Stable
+from .stable import Stable, RADIUS
 
 def set_integrator(num_points):
     global integrate
@@ -34,24 +34,42 @@ def log_prob(self, value):
         beta = self.skew.double()
         value = value.double()
 
-        # Optionally convert from Nolan's parametrization S^0 where samples depend
-        # continuously on (alpha,beta), allowing interpolation around the hole at
-        # alpha=1.
-        if self.coords == "S0":
-            value = value + beta * (math.pi / 2 * alpha).tan()
-        elif self.coords != "S":
-            raise ValueError("Unknown coords: {}".format(self.coords))
+        log_prob = _unsafe_alpha_stable_log_prob(alpha, beta, value, self.coords)
 
-        # Integration is not valid for very small values
-        value = torch.where(value > 0, value.clamp(min=0.01), value.clamp(max=-0.01))
+        return log_prob - self.scale.log()
 
-        log_prob = _unsafe_stable_log_prob(alpha, beta, value)
 
-        return log_prob - self.scale.log()
+def _unsafe_alpha_stable_log_prob(alpha, beta, Z, coords):
+    # Calculate log-probability of Z. This will fail if alpha is close to 1
+
+    # Optionally convert from Nolan's parametrization S^0 where samples depend
+    # continuously on (alpha,beta), allowing interpolation around the hole at
+    # alpha=1.
+    if coords == "S0":
+        Z = Z + beta * (math.pi / 2 * alpha).tan()
+    elif coords != "S":
+        raise ValueError("Unknown coords: {}".format(coords))
+    
+    # Find near zero values
+    idx = Z.abs() < RADIUS
+
+    # Calculate log-prob at safe values
+    log_prob = _unsafe_stable_log_prob(alpha, beta, torch.where(idx, RADIUS, Z))
+
+    # Handle small values by interpolation
+    if idx.any():
+        log_prob_pos = log_prob[idx]
+        log_prob_neg = _unsafe_stable_log_prob(alpha[idx], beta[idx],
+                                               -RADIUS * log_prob_pos.new_ones(log_prob_pos.shape))
+        weights = Z[idx] / (2 * RADIUS) + 0.5
+        log_prob[idx] = torch.logsumexp(torch.stack((log_prob_pos + weights.log(),
+                                                     log_prob_neg + (1 - weights).log()), dim=0), dim=0)
+        
+    return log_prob
 
 
 def _unsafe_stable_log_prob(alpha, beta, Z):
-    # Calculate log-probability of Z given V. This will fail if alpha is close to 1
+    # Calculate log-probability of Z. This will fail if alpha is close to 1
     # or if Z is close to 0
     ha = math.pi / 2 * alpha
     b = beta * ha.tan()

From 695ee8cf9720d4098adbb2a075744f47d578ce34 Mon Sep 17 00:00:00 2001
From: Ben Zickel <ben@vesttoo.com>
Date: Sun, 19 May 2024 15:16:13 +0300
Subject: [PATCH 03/24] Make Stable distribution log-probability calculation
 safe at alpha near one.

---
 pyro/distributions/stable_with_log_prob.py | 52 +++++++++++++++-------
 1 file changed, 37 insertions(+), 15 deletions(-)

diff --git a/pyro/distributions/stable_with_log_prob.py b/pyro/distributions/stable_with_log_prob.py
index b6a338ec5b..fa902b4d03 100644
--- a/pyro/distributions/stable_with_log_prob.py
+++ b/pyro/distributions/stable_with_log_prob.py
@@ -5,7 +5,12 @@
 
 from scipy.special import roots_legendre
 
-from .stable import Stable, RADIUS
+from .stable import Stable
+
+
+value_near_zero_tolerance = 0.01
+alpha_near_one_tolerance = 0.05
+
 
 def set_integrator(num_points):
     global integrate
@@ -34,34 +39,51 @@ def log_prob(self, value):
         beta = self.skew.double()
         value = value.double()
 
-        log_prob = _unsafe_alpha_stable_log_prob(alpha, beta, value, self.coords)
-
+        # Convert to Nolan's parametrization S^0 where samples depend
+        # continuously on (alpha,beta), allowing interpolation around the hole at
+        # alpha=1.
+        if self.coords == "S":
+            value = torch.where(alpha == 1, value, value - beta * (math.pi / 2 * alpha)).tan()
+        elif self.coords != "S0":
+            raise ValueError("Unknown coords: {}".format(self.coords))
+    
+        # Find near one alpha
+        idx = (alpha - 1).abs() < alpha_near_one_tolerance
+
+        log_prob = _unsafe_alpha_stable_log_prob_S0(torch.where(idx, 1 + alpha_near_one_tolerance, alpha), beta, value)
+
+        # Handle small values by interpolation
+        if idx.any():
+            log_prob_pos = log_prob[idx]
+            log_prob_neg = _unsafe_alpha_stable_log_prob_S0(
+                (1 - alpha_near_one_tolerance) * log_prob_pos.new_ones(log_prob_pos.shape), beta[idx], value[idx])
+            weights = (alpha[idx] - 1) / (2 * alpha_near_one_tolerance) + 0.5
+            log_prob[idx] = torch.logsumexp(torch.stack((log_prob_pos + weights.log(),
+                                                         log_prob_neg + (1 - weights).log()), dim=0), dim=0)
+        
         return log_prob - self.scale.log()
 
 
-def _unsafe_alpha_stable_log_prob(alpha, beta, Z, coords):
-    # Calculate log-probability of Z. This will fail if alpha is close to 1
+def _unsafe_alpha_stable_log_prob_S0(alpha, beta, Z):
+    # Calculate log-probability of Z in Nolan's parametrization S^0. This will fail if alpha is close to 1
 
-    # Optionally convert from Nolan's parametrization S^0 where samples depend
+    # Convert from Nolan's parametrization S^0 where samples depend
     # continuously on (alpha,beta), allowing interpolation around the hole at
     # alpha=1.
-    if coords == "S0":
-        Z = Z + beta * (math.pi / 2 * alpha).tan()
-    elif coords != "S":
-        raise ValueError("Unknown coords: {}".format(coords))
+    Z = Z + beta * (math.pi / 2 * alpha).tan()
     
     # Find near zero values
-    idx = Z.abs() < RADIUS
+    idx = Z.abs() < value_near_zero_tolerance
 
     # Calculate log-prob at safe values
-    log_prob = _unsafe_stable_log_prob(alpha, beta, torch.where(idx, RADIUS, Z))
+    log_prob = _unsafe_stable_log_prob(alpha, beta, torch.where(idx, value_near_zero_tolerance, Z))
 
     # Handle small values by interpolation
     if idx.any():
         log_prob_pos = log_prob[idx]
         log_prob_neg = _unsafe_stable_log_prob(alpha[idx], beta[idx],
-                                               -RADIUS * log_prob_pos.new_ones(log_prob_pos.shape))
-        weights = Z[idx] / (2 * RADIUS) + 0.5
+                                               -value_near_zero_tolerance * log_prob_pos.new_ones(log_prob_pos.shape))
+        weights = Z[idx] / (2 * value_near_zero_tolerance) + 0.5
         log_prob[idx] = torch.logsumexp(torch.stack((log_prob_pos + weights.log(),
                                                      log_prob_neg + (1 - weights).log()), dim=0), dim=0)
         
@@ -110,4 +132,4 @@ def _unsafe_stable_given_uniform_log_prob(V, alpha, beta, Z):
           * (v - V).cos().clamp(min=eps) 
         )
 
-    return -W + (alpha * W / Z / (alpha - 1)).abs().log()
\ No newline at end of file
+    return torch.where(W==torch.inf, -torch.inf, -W + (alpha * W / Z / (alpha - 1)).abs().log())
\ No newline at end of file

From 6d50cca921adf371ad3e28fb30da4d82c41254f9 Mon Sep 17 00:00:00 2001
From: Ben Zickel <ben@vesttoo.com>
Date: Sun, 19 May 2024 17:52:13 +0300
Subject: [PATCH 04/24] Make Stable log-probability method part of an
 independent class.

---
 pyro/distributions/__init__.py                |  5 +-
 ...le_with_log_prob.py => stable_log_prob.py} | 54 ++++++++++---------
 2 files changed, 32 insertions(+), 27 deletions(-)
 rename pyro/distributions/{stable_with_log_prob.py => stable_log_prob.py} (74%)

diff --git a/pyro/distributions/__init__.py b/pyro/distributions/__init__.py
index a43852334a..e6032bf985 100644
--- a/pyro/distributions/__init__.py
+++ b/pyro/distributions/__init__.py
@@ -120,7 +120,7 @@
 from pyro.distributions.softlaplace import SoftLaplace
 from pyro.distributions.spanning_tree import SpanningTree
 from pyro.distributions.stable import Stable
-from pyro.distributions.stable_with_log_prob import StableWithLogProb
+from pyro.distributions.stable_log_prob import StableLogProb
 from pyro.distributions.torch import __all__ as torch_dists
 from pyro.distributions.torch_distribution import (
     ExpandedDistribution,
@@ -143,6 +143,9 @@
 
 from . import constraints, kl, transforms
 
+class StableWithLogProb(StableLogProb, Stable):
+    pass
+
 __all__ = [
     "AVFMultivariateNormal",
     "AffineBeta",
diff --git a/pyro/distributions/stable_with_log_prob.py b/pyro/distributions/stable_log_prob.py
similarity index 74%
rename from pyro/distributions/stable_with_log_prob.py
rename to pyro/distributions/stable_log_prob.py
index fa902b4d03..5843f068a4 100644
--- a/pyro/distributions/stable_with_log_prob.py
+++ b/pyro/distributions/stable_log_prob.py
@@ -5,8 +5,6 @@
 
 from scipy.special import roots_legendre
 
-from .stable import Stable
-
 
 value_near_zero_tolerance = 0.01
 alpha_near_one_tolerance = 0.05
@@ -29,7 +27,7 @@ def integrate(fn, domain):
 set_integrator(num_points=501)
 
 
-class StableWithLogProb(Stable):
+class StableLogProb:
     def log_prob(self, value):
         # Undo shift and scale
         value = (value - self.loc) / self.scale
@@ -38,30 +36,34 @@ def log_prob(self, value):
         alpha = self.stability.double()
         beta = self.skew.double()
         value = value.double()
-
-        # Convert to Nolan's parametrization S^0 where samples depend
-        # continuously on (alpha,beta), allowing interpolation around the hole at
-        # alpha=1.
-        if self.coords == "S":
-            value = torch.where(alpha == 1, value, value - beta * (math.pi / 2 * alpha)).tan()
-        elif self.coords != "S0":
-            raise ValueError("Unknown coords: {}".format(self.coords))
-    
-        # Find near one alpha
-        idx = (alpha - 1).abs() < alpha_near_one_tolerance
-
-        log_prob = _unsafe_alpha_stable_log_prob_S0(torch.where(idx, 1 + alpha_near_one_tolerance, alpha), beta, value)
-
-        # Handle small values by interpolation
-        if idx.any():
-            log_prob_pos = log_prob[idx]
-            log_prob_neg = _unsafe_alpha_stable_log_prob_S0(
-                (1 - alpha_near_one_tolerance) * log_prob_pos.new_ones(log_prob_pos.shape), beta[idx], value[idx])
-            weights = (alpha[idx] - 1) / (2 * alpha_near_one_tolerance) + 0.5
-            log_prob[idx] = torch.logsumexp(torch.stack((log_prob_pos + weights.log(),
-                                                         log_prob_neg + (1 - weights).log()), dim=0), dim=0)
         
-        return log_prob - self.scale.log()
+        return _stable_log_prob(alpha, beta, value, self.coords) - self.scale.log()
+
+
+def _stable_log_prob(alpha, beta, value, coords):
+    # Convert to Nolan's parametrization S^0 where samples depend
+    # continuously on (alpha,beta), allowing interpolation around the hole at
+    # alpha=1.
+    if coords == "S":
+        value = torch.where(alpha == 1, value, value - beta * (math.pi / 2 * alpha)).tan()
+    elif coords != "S0":
+        raise ValueError("Unknown coords: {}".format(coords))
+
+    # Find near one alpha
+    idx = (alpha - 1).abs() < alpha_near_one_tolerance
+
+    log_prob = _unsafe_alpha_stable_log_prob_S0(torch.where(idx, 1 + alpha_near_one_tolerance, alpha), beta, value)
+
+    # Handle small values by interpolation
+    if idx.any():
+        log_prob_pos = log_prob[idx]
+        log_prob_neg = _unsafe_alpha_stable_log_prob_S0(
+            (1 - alpha_near_one_tolerance) * log_prob_pos.new_ones(log_prob_pos.shape), beta[idx], value[idx])
+        weights = (alpha[idx] - 1) / (2 * alpha_near_one_tolerance) + 0.5
+        log_prob[idx] = torch.logsumexp(torch.stack((log_prob_pos + weights.log(),
+                                                        log_prob_neg + (1 - weights).log()), dim=0), dim=0)
+
+    return log_prob
 
 
 def _unsafe_alpha_stable_log_prob_S0(alpha, beta, Z):

From 7661a6ef4f5b7542412973239951e1487052f029 Mon Sep 17 00:00:00 2001
From: Ben Zickel <ben@vesttoo.com>
Date: Mon, 20 May 2024 00:31:04 +0300
Subject: [PATCH 05/24] Added dynamic near zero value tolerance to the
 log-probability estimation of the Stable distribution.

---
 pyro/distributions/stable_log_prob.py | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/pyro/distributions/stable_log_prob.py b/pyro/distributions/stable_log_prob.py
index 5843f068a4..19161f795a 100644
--- a/pyro/distributions/stable_log_prob.py
+++ b/pyro/distributions/stable_log_prob.py
@@ -6,7 +6,7 @@
 from scipy.special import roots_legendre
 
 
-value_near_zero_tolerance = 0.01
+value_near_zero_tolerance = 0.05
 alpha_near_one_tolerance = 0.05
 
 
@@ -75,17 +75,18 @@ def _unsafe_alpha_stable_log_prob_S0(alpha, beta, Z):
     Z = Z + beta * (math.pi / 2 * alpha).tan()
     
     # Find near zero values
-    idx = Z.abs() < value_near_zero_tolerance
+    per_alpha_value_near_zero_tolerance = value_near_zero_tolerance * alpha / (1 - alpha).abs()
+    idx = Z.abs() < per_alpha_value_near_zero_tolerance
 
     # Calculate log-prob at safe values
-    log_prob = _unsafe_stable_log_prob(alpha, beta, torch.where(idx, value_near_zero_tolerance, Z))
+    log_prob = _unsafe_stable_log_prob(alpha, beta, torch.where(idx, per_alpha_value_near_zero_tolerance, Z))
 
     # Handle small values by interpolation
     if idx.any():
         log_prob_pos = log_prob[idx]
         log_prob_neg = _unsafe_stable_log_prob(alpha[idx], beta[idx],
-                                               -value_near_zero_tolerance * log_prob_pos.new_ones(log_prob_pos.shape))
-        weights = Z[idx] / (2 * value_near_zero_tolerance) + 0.5
+                                               -per_alpha_value_near_zero_tolerance[idx] * log_prob_pos.new_ones(log_prob_pos.shape))
+        weights = Z[idx] / (2 * per_alpha_value_near_zero_tolerance[idx]) + 0.5
         log_prob[idx] = torch.logsumexp(torch.stack((log_prob_pos + weights.log(),
                                                      log_prob_neg + (1 - weights).log()), dim=0), dim=0)
         

From 2e2b036970ce8e0e039a3d23ee7b69327126024b Mon Sep 17 00:00:00 2001
From: Ben Zickel <ben@vesttoo.com>
Date: Mon, 20 May 2024 01:06:07 +0300
Subject: [PATCH 06/24] Reduce Stable log-probability calculation value near
 zero tolerance in order to improve convergence.

---
 pyro/distributions/stable_log_prob.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyro/distributions/stable_log_prob.py b/pyro/distributions/stable_log_prob.py
index 19161f795a..3d2c09a83d 100644
--- a/pyro/distributions/stable_log_prob.py
+++ b/pyro/distributions/stable_log_prob.py
@@ -6,7 +6,7 @@
 from scipy.special import roots_legendre
 
 
-value_near_zero_tolerance = 0.05
+value_near_zero_tolerance = 0.01
 alpha_near_one_tolerance = 0.05
 
 

From cd6dde321d1d469a747cd5219f3c759bfd5617ad Mon Sep 17 00:00:00 2001
From: Ben Zickel <ben@vesttoo.com>
Date: Mon, 20 May 2024 02:51:22 +0300
Subject: [PATCH 07/24] Cap range of Stable log-probability.

---
 pyro/distributions/stable_log_prob.py | 14 +++++++++++++-
 1 file changed, 13 insertions(+), 1 deletion(-)

diff --git a/pyro/distributions/stable_log_prob.py b/pyro/distributions/stable_log_prob.py
index 3d2c09a83d..ccb6944c15 100644
--- a/pyro/distributions/stable_log_prob.py
+++ b/pyro/distributions/stable_log_prob.py
@@ -10,6 +10,11 @@
 alpha_near_one_tolerance = 0.05
 
 
+finfo = torch.finfo(torch.float64)
+MAX_LOG = math.log10(finfo.max)
+MIN_LOG = math.log10(finfo.tiny)
+
+
 def set_integrator(num_points):
     global integrate
     roots, weights = roots_legendre(num_points)
@@ -129,10 +134,17 @@ def _unsafe_stable_given_uniform_log_prob(V, alpha, beta, Z):
     
     # +/- `ha` term to keep the precision of alpha * (V + half_pi) when V ~ -half_pi
     v = atan_b - ha + alpha * (V + half_pi)
+
     W = ( ( v.sin() / Z /
            (atan_b.cos() * V.cos()).pow(inv_alpha)
           ).pow(alpha / (1 - alpha))
           * (v - V).cos().clamp(min=eps) 
         )
 
-    return torch.where(W==torch.inf, -torch.inf, -W + (alpha * W / Z / (alpha - 1)).abs().log())
\ No newline at end of file
+    log_prob = -W + (alpha * W / Z / (alpha - 1)).abs().log()
+
+    # Range limiting in order to eliminate zero probability
+    log_prob = torch.where(W==torch.inf, -torch.inf, log_prob)
+    log_prob = log_prob.clamp(min=MIN_LOG, max=MAX_LOG)
+
+    return log_prob
\ No newline at end of file

From 49657f7bc0575dab60cb6373526725c74fa094d6 Mon Sep 17 00:00:00 2001
From: Ben Zickel <ben@vesttoo.com>
Date: Mon, 20 May 2024 08:54:23 +0300
Subject: [PATCH 08/24] Clamp log in order to make gradient continuous.

---
 pyro/distributions/stable_log_prob.py | 20 ++++++++++++--------
 1 file changed, 12 insertions(+), 8 deletions(-)

diff --git a/pyro/distributions/stable_log_prob.py b/pyro/distributions/stable_log_prob.py
index ccb6944c15..f944d82d62 100644
--- a/pyro/distributions/stable_log_prob.py
+++ b/pyro/distributions/stable_log_prob.py
@@ -123,7 +123,7 @@ def _unsafe_stable_log_prob(alpha, beta, Z):
 def _unsafe_stable_given_uniform_log_prob(V, alpha, beta, Z):
     # Calculate log-probability of Z given V. This will fail if alpha is close to 1
     # or if Z is close to 0
-    inv_alpha = alpha.reciprocal()
+    inv_alpha_minus_one = (alpha - 1).reciprocal()
     half_pi = math.pi / 2
     eps = torch.finfo(V.dtype).eps
     # make V belong to the open interval (-pi/2, pi/2)
@@ -131,20 +131,24 @@ def _unsafe_stable_given_uniform_log_prob(V, alpha, beta, Z):
     ha = half_pi * alpha
     b = beta * ha.tan()
     atan_b = b.atan()
-    
+    cos_V = V.cos()
+
     # +/- `ha` term to keep the precision of alpha * (V + half_pi) when V ~ -half_pi
     v = atan_b - ha + alpha * (V + half_pi)
 
-    W = ( ( v.sin() / Z /
-           (atan_b.cos() * V.cos()).pow(inv_alpha)
-          ).pow(alpha / (1 - alpha))
-          * (v - V).cos().clamp(min=eps) 
-        )
+    term1_log = atan_b.cos().log() * inv_alpha_minus_one
+    term2_log = (Z * cos_V / v.sin()).log() * alpha * inv_alpha_minus_one
+    term3_log = ((v - V).cos() / cos_V).log()
+
+    W_log = term1_log + term2_log + term3_log
+
+    W = W_log.clamp(min=MIN_LOG, max=MAX_LOG).exp()
 
     log_prob = -W + (alpha * W / Z / (alpha - 1)).abs().log()
 
-    # Range limiting in order to eliminate zero probability
+    # Infinite W means zero-probability
     log_prob = torch.where(W==torch.inf, -torch.inf, log_prob)
+
     log_prob = log_prob.clamp(min=MIN_LOG, max=MAX_LOG)
 
     return log_prob
\ No newline at end of file

From 147a772b887ce84adf2f02d71db471ca092515f5 Mon Sep 17 00:00:00 2001
From: Ben Zickel <ben@vesttoo.com>
Date: Mon, 20 May 2024 12:10:45 +0300
Subject: [PATCH 09/24] Code cleanup.

---
 pyro/distributions/stable_log_prob.py | 18 +++++++++++-------
 1 file changed, 11 insertions(+), 7 deletions(-)

diff --git a/pyro/distributions/stable_log_prob.py b/pyro/distributions/stable_log_prob.py
index f944d82d62..86b88ae6be 100644
--- a/pyro/distributions/stable_log_prob.py
+++ b/pyro/distributions/stable_log_prob.py
@@ -15,8 +15,7 @@
 MIN_LOG = math.log10(finfo.tiny)
 
 
-def set_integrator(num_points):
-    global integrate
+def create_integrator(num_points):
     roots, weights = roots_legendre(num_points)
     roots = torch.Tensor(roots).double()
     weights = torch.Tensor(weights).double()
@@ -27,6 +26,12 @@ def integrate(fn, domain):
         half_roots_sl = half_roots[sl]
         value = domain[0] * (0.5 - half_roots_sl) + domain[1] * (0.5 + half_roots_sl)
         return torch.logsumexp(fn(value) + log_weights[sl], dim=0) + ((domain[1] - domain[0]) / 2).log()
+    return integrate
+
+
+def set_integrator(num_points):
+    global integrate
+    integrate = create_integrator(num_points)
 
 
 set_integrator(num_points=501)
@@ -59,14 +64,14 @@ def _stable_log_prob(alpha, beta, value, coords):
 
     log_prob = _unsafe_alpha_stable_log_prob_S0(torch.where(idx, 1 + alpha_near_one_tolerance, alpha), beta, value)
 
-    # Handle small values by interpolation
+    # Handle alpha near one by interpolation
     if idx.any():
         log_prob_pos = log_prob[idx]
         log_prob_neg = _unsafe_alpha_stable_log_prob_S0(
             (1 - alpha_near_one_tolerance) * log_prob_pos.new_ones(log_prob_pos.shape), beta[idx], value[idx])
         weights = (alpha[idx] - 1) / (2 * alpha_near_one_tolerance) + 0.5
         log_prob[idx] = torch.logsumexp(torch.stack((log_prob_pos + weights.log(),
-                                                        log_prob_neg + (1 - weights).log()), dim=0), dim=0)
+                                                     log_prob_neg + (1 - weights).log()), dim=0), dim=0)
 
     return log_prob
 
@@ -86,11 +91,10 @@ def _unsafe_alpha_stable_log_prob_S0(alpha, beta, Z):
     # Calculate log-prob at safe values
     log_prob = _unsafe_stable_log_prob(alpha, beta, torch.where(idx, per_alpha_value_near_zero_tolerance, Z))
 
-    # Handle small values by interpolation
+    # Handle near zero values by interpolation
     if idx.any():
         log_prob_pos = log_prob[idx]
-        log_prob_neg = _unsafe_stable_log_prob(alpha[idx], beta[idx],
-                                               -per_alpha_value_near_zero_tolerance[idx] * log_prob_pos.new_ones(log_prob_pos.shape))
+        log_prob_neg = _unsafe_stable_log_prob(alpha[idx], beta[idx], -per_alpha_value_near_zero_tolerance[idx])
         weights = Z[idx] / (2 * per_alpha_value_near_zero_tolerance[idx]) + 0.5
         log_prob[idx] = torch.logsumexp(torch.stack((log_prob_pos + weights.log(),
                                                      log_prob_neg + (1 - weights).log()), dim=0), dim=0)

From d493f8c087ee46b0acfd317100b9a098ef5c5daf Mon Sep 17 00:00:00 2001
From: Ben Zickel <ben@vesttoo.com>
Date: Mon, 20 May 2024 13:51:20 +0300
Subject: [PATCH 10/24] Don't reparametrize
 pyro.distributions.StableWithLogProb.

---
 pyro/infer/reparam/strategies.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyro/infer/reparam/strategies.py b/pyro/infer/reparam/strategies.py
index 4a471caaed..ae0d92f73c 100644
--- a/pyro/infer/reparam/strategies.py
+++ b/pyro/infer/reparam/strategies.py
@@ -114,7 +114,7 @@ def _minimal_reparam(fn, is_observed):
                 return TransformReparam()  # Then reparametrize new sites.
         fn = fn.base_dist
 
-    if isinstance(fn, dist.Stable):
+    if isinstance(fn, dist.Stable) and not isinstance(fn, dist.StableWithLogProb):
         if not is_observed:
             return LatentStableReparam()
         elif fn.skew.requires_grad or fn.skew.any():

From 037f0941461748907af2a1fed0f99f061eaa6599 Mon Sep 17 00:00:00 2001
From: Ben Zickel <ben@vesttoo.com>
Date: Mon, 20 May 2024 17:36:32 +0300
Subject: [PATCH 11/24] Add tests for Stable distribution with method for
 calculating the log-probability.

---
 .../test_stable_with_log_prob.py              | 94 +++++++++++++++++++
 1 file changed, 94 insertions(+)
 create mode 100644 tests/distributions/test_stable_with_log_prob.py

diff --git a/tests/distributions/test_stable_with_log_prob.py b/tests/distributions/test_stable_with_log_prob.py
new file mode 100644
index 0000000000..e7bd4e2903
--- /dev/null
+++ b/tests/distributions/test_stable_with_log_prob.py
@@ -0,0 +1,94 @@
+import logging
+
+import pytest
+import pyro
+import torch
+
+from pyro.distributions import StableWithLogProb as Stable
+from pyro.distributions import constraints
+from pyro.infer import SVI, Trace_ELBO
+from pyro.infer.autoguide import AutoNormal
+
+from tests.common import assert_close
+
+
+torch.set_default_dtype(torch.float64)
+
+
+@pytest.mark.parametrize(
+    "alpha, beta, c, mu",
+    [
+        (1.00,   0.8,  2.0,  3.0),
+        (1.02,  -0.8,  2.0, -3.0),
+        (0.98,   0.5,  1.0, -3.0),
+        (0.95,  -0.5,  1.0,  3.0),
+        (1.10,   0.0,  1.0,  0.0),
+        (1.80,  -0.5,  1.0, -2.0),
+        (0.50,   0.0,  1.0,  2.0),
+    ],
+)
+@pytest.mark.parametrize(
+    "alpha_0, beta_0, c_0, mu_0",
+    [
+        (1.3, 0.0, 1.0, 0.0),
+    ],
+)
+def test_stable_with_log_prob_param_fit(alpha, beta, c, mu, alpha_0, beta_0, c_0, mu_0):
+    # Sample test data
+    n = 10000
+    pyro.set_rng_seed(20240520)
+    data = Stable(alpha, beta, c, mu).sample((n,))
+
+    def model(data):    
+        alpha = pyro.param("alpha", torch.tensor(alpha_0), constraint=constraints.interval(0, 2))
+        beta = pyro.param("beta", torch.tensor(beta_0), constraint=constraints.interval(-1, 1))
+        c = pyro.param("c", torch.tensor(c_0), constraint=constraints.positive)
+        mu = pyro.param("mu", torch.tensor(mu_0), constraint=constraints.real)
+        with pyro.plate("data", data.shape[0]):
+            pyro.sample("obs", Stable(alpha, beta, c, mu), obs=data)
+
+    def train(model, guide, num_steps=300, lr=0.03):
+        pyro.clear_param_store()
+        pyro.set_rng_seed(20240520)
+
+        # set up ELBO, and optimizer
+        elbo = Trace_ELBO()
+        elbo.loss(model, guide, data=data)
+        optim = pyro.optim.Adam({"lr": lr})
+        svi = SVI(model, guide, optim, loss=elbo)
+
+        # optimize
+        for i in range(num_steps):
+            loss = svi.step(data) / data.numel()
+            if i % 10 == 0:
+                logging.info(f"step {i} loss = {loss:0.6g}")
+                log_progress()
+
+        logging.info(f"Parameter estimates (n = {n}):")
+        log_progress()
+
+    def log_progress():
+        logging.info(f"alpha: Estimate = {pyro.param('alpha')}, true = {alpha}")
+        logging.info(f"beta: Estimate = {pyro.param('beta')}, true = {beta}")
+        logging.info(f"c: Estimate = {pyro.param('c')}, true = {c}")
+        logging.info(f"mu: Estimate = {pyro.param('mu')}, true = {mu}")
+
+    # Fit model to data
+    guide = AutoNormal(model)
+    train(model, guide);
+
+    # Verify fit accuracy
+    assert_close(alpha, pyro.param('alpha').item(), atol=0.03)
+    assert_close(beta, pyro.param('beta').item(), atol=0.04)
+    assert_close(c, pyro.param('c').item(), atol=0.2)
+    assert_close(mu, pyro.param('mu').item(), atol=0.2)
+
+
+# # The below tests will be executed:
+# test_stable_with_log_prob_param_fit(1.00,   0.8,  2.0,  3.0, 1.3, 0.0, 1.0, 0.0)
+# test_stable_with_log_prob_param_fit(1.02,  -0.8,  2.0, -3.0, 1.3, 0.0, 1.0, 0.0)
+# test_stable_with_log_prob_param_fit(0.98,   0.5,  1.0, -3.0, 1.3, 0.0, 1.0, 0.0)
+# test_stable_with_log_prob_param_fit(0.95,  -0.5,  1.0,  3.0, 1.3, 0.0, 1.0, 0.0)
+# test_stable_with_log_prob_param_fit(1.10,   0.0,  1.0,  0.0, 1.3, 0.0, 1.0, 0.0)
+# test_stable_with_log_prob_param_fit(1.80,  -0.5,  1.0, -2.0, 1.3, 0.0, 1.0, 0.0)
+# test_stable_with_log_prob_param_fit(0.50,   0.0,  1.0,  2.0, 1.3, 0.0, 1.0, 0.0)

From 1f0a696508f71c250dd916eac7a269f0810ccf99 Mon Sep 17 00:00:00 2001
From: Ben Zickel <ben@vesttoo.com>
Date: Mon, 20 May 2024 19:15:27 +0300
Subject: [PATCH 12/24] Linting and formatting.

---
 pyro/distributions/__init__.py                |  2 +
 pyro/distributions/stable_log_prob.py         | 75 +++++++++++++------
 .../test_stable_with_log_prob.py              | 41 +++++-----
 3 files changed, 78 insertions(+), 40 deletions(-)

diff --git a/pyro/distributions/__init__.py b/pyro/distributions/__init__.py
index e6032bf985..6fcaa0933b 100644
--- a/pyro/distributions/__init__.py
+++ b/pyro/distributions/__init__.py
@@ -143,9 +143,11 @@
 
 from . import constraints, kl, transforms
 
+
 class StableWithLogProb(StableLogProb, Stable):
     pass
 
+
 __all__ = [
     "AVFMultivariateNormal",
     "AffineBeta",
diff --git a/pyro/distributions/stable_log_prob.py b/pyro/distributions/stable_log_prob.py
index 86b88ae6be..1ad9f7d3ce 100644
--- a/pyro/distributions/stable_log_prob.py
+++ b/pyro/distributions/stable_log_prob.py
@@ -1,11 +1,12 @@
-import torch
-import math
+# Copyright Contributors to the Pyro project.
+# SPDX-License-Identifier: Apache-2.0
 
+import math
 from functools import partial
 
+import torch
 from scipy.special import roots_legendre
 
-
 value_near_zero_tolerance = 0.01
 alpha_near_one_tolerance = 0.05
 
@@ -21,11 +22,16 @@ def create_integrator(num_points):
     weights = torch.Tensor(weights).double()
     log_weights = weights.log()
     half_roots = roots * 0.5
+
     def integrate(fn, domain):
         sl = [slice(None)] + (len(domain.shape) - 1) * [None]
         half_roots_sl = half_roots[sl]
         value = domain[0] * (0.5 - half_roots_sl) + domain[1] * (0.5 + half_roots_sl)
-        return torch.logsumexp(fn(value) + log_weights[sl], dim=0) + ((domain[1] - domain[0]) / 2).log()
+        return (
+            torch.logsumexp(fn(value) + log_weights[sl], dim=0)
+            + ((domain[1] - domain[0]) / 2).log()
+        )
+
     return integrate
 
 
@@ -46,7 +52,7 @@ def log_prob(self, value):
         alpha = self.stability.double()
         beta = self.skew.double()
         value = value.double()
-        
+
         return _stable_log_prob(alpha, beta, value, self.coords) - self.scale.log()
 
 
@@ -55,23 +61,35 @@ def _stable_log_prob(alpha, beta, value, coords):
     # continuously on (alpha,beta), allowing interpolation around the hole at
     # alpha=1.
     if coords == "S":
-        value = torch.where(alpha == 1, value, value - beta * (math.pi / 2 * alpha)).tan()
+        value = torch.where(
+            alpha == 1, value, value - beta * (math.pi / 2 * alpha)
+        ).tan()
     elif coords != "S0":
         raise ValueError("Unknown coords: {}".format(coords))
 
     # Find near one alpha
     idx = (alpha - 1).abs() < alpha_near_one_tolerance
 
-    log_prob = _unsafe_alpha_stable_log_prob_S0(torch.where(idx, 1 + alpha_near_one_tolerance, alpha), beta, value)
+    log_prob = _unsafe_alpha_stable_log_prob_S0(
+        torch.where(idx, 1 + alpha_near_one_tolerance, alpha), beta, value
+    )
 
     # Handle alpha near one by interpolation
     if idx.any():
         log_prob_pos = log_prob[idx]
         log_prob_neg = _unsafe_alpha_stable_log_prob_S0(
-            (1 - alpha_near_one_tolerance) * log_prob_pos.new_ones(log_prob_pos.shape), beta[idx], value[idx])
+            (1 - alpha_near_one_tolerance) * log_prob_pos.new_ones(log_prob_pos.shape),
+            beta[idx],
+            value[idx],
+        )
         weights = (alpha[idx] - 1) / (2 * alpha_near_one_tolerance) + 0.5
-        log_prob[idx] = torch.logsumexp(torch.stack((log_prob_pos + weights.log(),
-                                                     log_prob_neg + (1 - weights).log()), dim=0), dim=0)
+        log_prob[idx] = torch.logsumexp(
+            torch.stack(
+                (log_prob_pos + weights.log(), log_prob_neg + (1 - weights).log()),
+                dim=0,
+            ),
+            dim=0,
+        )
 
     return log_prob
 
@@ -83,22 +101,33 @@ def _unsafe_alpha_stable_log_prob_S0(alpha, beta, Z):
     # continuously on (alpha,beta), allowing interpolation around the hole at
     # alpha=1.
     Z = Z + beta * (math.pi / 2 * alpha).tan()
-    
+
     # Find near zero values
-    per_alpha_value_near_zero_tolerance = value_near_zero_tolerance * alpha / (1 - alpha).abs()
+    per_alpha_value_near_zero_tolerance = (
+        value_near_zero_tolerance * alpha / (1 - alpha).abs()
+    )
     idx = Z.abs() < per_alpha_value_near_zero_tolerance
 
     # Calculate log-prob at safe values
-    log_prob = _unsafe_stable_log_prob(alpha, beta, torch.where(idx, per_alpha_value_near_zero_tolerance, Z))
+    log_prob = _unsafe_stable_log_prob(
+        alpha, beta, torch.where(idx, per_alpha_value_near_zero_tolerance, Z)
+    )
 
     # Handle near zero values by interpolation
     if idx.any():
         log_prob_pos = log_prob[idx]
-        log_prob_neg = _unsafe_stable_log_prob(alpha[idx], beta[idx], -per_alpha_value_near_zero_tolerance[idx])
+        log_prob_neg = _unsafe_stable_log_prob(
+            alpha[idx], beta[idx], -per_alpha_value_near_zero_tolerance[idx]
+        )
         weights = Z[idx] / (2 * per_alpha_value_near_zero_tolerance[idx]) + 0.5
-        log_prob[idx] = torch.logsumexp(torch.stack((log_prob_pos + weights.log(),
-                                                     log_prob_neg + (1 - weights).log()), dim=0), dim=0)
-        
+        log_prob[idx] = torch.logsumexp(
+            torch.stack(
+                (log_prob_pos + weights.log(), log_prob_neg + (1 - weights).log()),
+                dim=0,
+            ),
+            dim=0,
+        )
+
     return log_prob
 
 
@@ -109,7 +138,7 @@ def _unsafe_stable_log_prob(alpha, beta, Z):
     b = beta * ha.tan()
     atan_b = b.atan()
     u_zero = -alpha.reciprocal() * atan_b
-    
+
     # If sample should be negative calculate with flipped beta and flipped value
     flip_beta_x = Z < 0
     beta = torch.where(flip_beta_x, -beta, beta)
@@ -119,8 +148,10 @@ def _unsafe_stable_log_prob(alpha, beta, Z):
     # Set integration domwin
     domain = torch.stack((u_zero, 0.5 * math.pi * u_zero.new_ones(u_zero.shape)), dim=0)
 
-    integrand = partial(_unsafe_stable_given_uniform_log_prob, alpha=alpha, beta=beta, Z=Z)
-    
+    integrand = partial(
+        _unsafe_stable_given_uniform_log_prob, alpha=alpha, beta=beta, Z=Z
+    )
+
     return integrate(integrand, domain) - math.log(math.pi)
 
 
@@ -151,8 +182,8 @@ def _unsafe_stable_given_uniform_log_prob(V, alpha, beta, Z):
     log_prob = -W + (alpha * W / Z / (alpha - 1)).abs().log()
 
     # Infinite W means zero-probability
-    log_prob = torch.where(W==torch.inf, -torch.inf, log_prob)
+    log_prob = torch.where(W == torch.inf, -torch.inf, log_prob)
 
     log_prob = log_prob.clamp(min=MIN_LOG, max=MAX_LOG)
 
-    return log_prob
\ No newline at end of file
+    return log_prob
diff --git a/tests/distributions/test_stable_with_log_prob.py b/tests/distributions/test_stable_with_log_prob.py
index e7bd4e2903..e99354d2c3 100644
--- a/tests/distributions/test_stable_with_log_prob.py
+++ b/tests/distributions/test_stable_with_log_prob.py
@@ -1,30 +1,31 @@
+# Copyright Contributors to the Pyro project.
+# SPDX-License-Identifier: Apache-2.0
+
 import logging
 
 import pytest
-import pyro
 import torch
 
+import pyro
 from pyro.distributions import StableWithLogProb as Stable
 from pyro.distributions import constraints
 from pyro.infer import SVI, Trace_ELBO
 from pyro.infer.autoguide import AutoNormal
-
 from tests.common import assert_close
 
-
 torch.set_default_dtype(torch.float64)
 
 
 @pytest.mark.parametrize(
     "alpha, beta, c, mu",
     [
-        (1.00,   0.8,  2.0,  3.0),
-        (1.02,  -0.8,  2.0, -3.0),
-        (0.98,   0.5,  1.0, -3.0),
-        (0.95,  -0.5,  1.0,  3.0),
-        (1.10,   0.0,  1.0,  0.0),
-        (1.80,  -0.5,  1.0, -2.0),
-        (0.50,   0.0,  1.0,  2.0),
+        (1.00, 0.8, 2.0, 3.0),
+        (1.02, -0.8, 2.0, -3.0),
+        (0.98, 0.5, 1.0, -3.0),
+        (0.95, -0.5, 1.0, 3.0),
+        (1.10, 0.0, 1.0, 0.0),
+        (1.80, -0.5, 1.0, -2.0),
+        (0.50, 0.0, 1.0, 2.0),
     ],
 )
 @pytest.mark.parametrize(
@@ -39,9 +40,13 @@ def test_stable_with_log_prob_param_fit(alpha, beta, c, mu, alpha_0, beta_0, c_0
     pyro.set_rng_seed(20240520)
     data = Stable(alpha, beta, c, mu).sample((n,))
 
-    def model(data):    
-        alpha = pyro.param("alpha", torch.tensor(alpha_0), constraint=constraints.interval(0, 2))
-        beta = pyro.param("beta", torch.tensor(beta_0), constraint=constraints.interval(-1, 1))
+    def model(data):
+        alpha = pyro.param(
+            "alpha", torch.tensor(alpha_0), constraint=constraints.interval(0, 2)
+        )
+        beta = pyro.param(
+            "beta", torch.tensor(beta_0), constraint=constraints.interval(-1, 1)
+        )
         c = pyro.param("c", torch.tensor(c_0), constraint=constraints.positive)
         mu = pyro.param("mu", torch.tensor(mu_0), constraint=constraints.real)
         with pyro.plate("data", data.shape[0]):
@@ -75,13 +80,13 @@ def log_progress():
 
     # Fit model to data
     guide = AutoNormal(model)
-    train(model, guide);
+    train(model, guide)
 
     # Verify fit accuracy
-    assert_close(alpha, pyro.param('alpha').item(), atol=0.03)
-    assert_close(beta, pyro.param('beta').item(), atol=0.04)
-    assert_close(c, pyro.param('c').item(), atol=0.2)
-    assert_close(mu, pyro.param('mu').item(), atol=0.2)
+    assert_close(alpha, pyro.param("alpha").item(), atol=0.03)
+    assert_close(beta, pyro.param("beta").item(), atol=0.04)
+    assert_close(c, pyro.param("c").item(), atol=0.2)
+    assert_close(mu, pyro.param("mu").item(), atol=0.2)
 
 
 # # The below tests will be executed:

From 2d2b7021347f3caae0f254911d0855d7520249c0 Mon Sep 17 00:00:00 2001
From: Ben Zickel <ben@vesttoo.com>
Date: Tue, 21 May 2024 10:04:00 +0300
Subject: [PATCH 13/24] Moved definition of StableWithLogProb into
 pyro.distributions.stable.

---
 pyro/distributions/__init__.py | 8 +-------
 pyro/distributions/stable.py   | 5 +++++
 2 files changed, 6 insertions(+), 7 deletions(-)

diff --git a/pyro/distributions/__init__.py b/pyro/distributions/__init__.py
index 6fcaa0933b..1f33b59666 100644
--- a/pyro/distributions/__init__.py
+++ b/pyro/distributions/__init__.py
@@ -119,8 +119,7 @@
 from pyro.distributions.sine_skewed import SineSkewed
 from pyro.distributions.softlaplace import SoftLaplace
 from pyro.distributions.spanning_tree import SpanningTree
-from pyro.distributions.stable import Stable
-from pyro.distributions.stable_log_prob import StableLogProb
+from pyro.distributions.stable import Stable, StableWithLogProb
 from pyro.distributions.torch import __all__ as torch_dists
 from pyro.distributions.torch_distribution import (
     ExpandedDistribution,
@@ -143,11 +142,6 @@
 
 from . import constraints, kl, transforms
 
-
-class StableWithLogProb(StableLogProb, Stable):
-    pass
-
-
 __all__ = [
     "AVFMultivariateNormal",
     "AffineBeta",
diff --git a/pyro/distributions/stable.py b/pyro/distributions/stable.py
index 0b2ec5b9c0..e508994f32 100644
--- a/pyro/distributions/stable.py
+++ b/pyro/distributions/stable.py
@@ -7,6 +7,7 @@
 from torch.distributions import constraints
 from torch.distributions.utils import broadcast_all
 
+from pyro.distributions.stable_log_prob import StableLogProb
 from pyro.distributions.torch_distribution import TorchDistribution
 
 
@@ -204,3 +205,7 @@ def mean(self):
     def variance(self):
         var = self.scale * self.scale
         return var.mul(2).masked_fill(self.stability < 2, math.inf)
+
+
+class StableWithLogProb(StableLogProb, Stable):
+    pass

From daf04a0c39b466d34aa88d4d79559d525ea0bd34 Mon Sep 17 00:00:00 2001
From: Ben Zickel <ben@vesttoo.com>
Date: Tue, 21 May 2024 11:56:58 +0300
Subject: [PATCH 14/24] Avoid importing scipy until StableWithLogProb.log_prob
 is called for the first time in order to avoid requiring scipy when building
 the docs.

---
 pyro/distributions/stable_log_prob.py | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/pyro/distributions/stable_log_prob.py b/pyro/distributions/stable_log_prob.py
index 1ad9f7d3ce..e71094ba73 100644
--- a/pyro/distributions/stable_log_prob.py
+++ b/pyro/distributions/stable_log_prob.py
@@ -5,7 +5,6 @@
 from functools import partial
 
 import torch
-from scipy.special import roots_legendre
 
 value_near_zero_tolerance = 0.01
 alpha_near_one_tolerance = 0.05
@@ -17,6 +16,8 @@
 
 
 def create_integrator(num_points):
+    from scipy.special import roots_legendre
+
     roots, weights = roots_legendre(num_points)
     roots = torch.Tensor(roots).double()
     weights = torch.Tensor(weights).double()
@@ -40,7 +41,11 @@ def set_integrator(num_points):
     integrate = create_integrator(num_points)
 
 
-set_integrator(num_points=501)
+# Stub which is replaced by the default integrator when called for the first time
+# if a default integrator has not already been set.
+def integrate(*args, **kwargs):
+    set_integrator(num_points=501)
+    return integrate(*args, **kwargs)
 
 
 class StableLogProb:

From 110ea37e0d512bbbc5a8610dff63c8dce38527fb Mon Sep 17 00:00:00 2001
From: Ben Zickel <ben@vesttoo.com>
Date: Tue, 21 May 2024 13:24:56 +0300
Subject: [PATCH 15/24] Don't allow reparameterization of StableWithLogProb.

---
 pyro/infer/reparam/stable.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyro/infer/reparam/stable.py b/pyro/infer/reparam/stable.py
index 670a10e256..251d3c4230 100644
--- a/pyro/infer/reparam/stable.py
+++ b/pyro/infer/reparam/stable.py
@@ -158,7 +158,7 @@ def apply(self, msg):
         is_observed = msg["is_observed"]
 
         fn, event_dim = self._unwrap(fn)
-        assert isinstance(fn, dist.Stable) and fn.coords == "S0"
+        assert isinstance(fn, dist.Stable) and fn.coords == "S0" and not isinstance(fn, dist.StableWithLogProb)
 
         # Strategy: Let X ~ S0(a,b,s,m) be the stable variable of interest.
         # 1. WLOG scale and shift so s=1 and m=0, additionally shifting to convert

From 2864c33ab4319afd7501c8290ff3e5b240f298ed Mon Sep 17 00:00:00 2001
From: Ben Zickel <ben@vesttoo.com>
Date: Tue, 21 May 2024 13:33:05 +0300
Subject: [PATCH 16/24] Linting and formatting.

---
 pyro/infer/reparam/stable.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/pyro/infer/reparam/stable.py b/pyro/infer/reparam/stable.py
index 251d3c4230..d6f792a524 100644
--- a/pyro/infer/reparam/stable.py
+++ b/pyro/infer/reparam/stable.py
@@ -158,7 +158,11 @@ def apply(self, msg):
         is_observed = msg["is_observed"]
 
         fn, event_dim = self._unwrap(fn)
-        assert isinstance(fn, dist.Stable) and fn.coords == "S0" and not isinstance(fn, dist.StableWithLogProb)
+        assert (
+            isinstance(fn, dist.Stable)
+            and fn.coords == "S0"
+            and not isinstance(fn, dist.StableWithLogProb)
+        )
 
         # Strategy: Let X ~ S0(a,b,s,m) be the stable variable of interest.
         # 1. WLOG scale and shift so s=1 and m=0, additionally shifting to convert

From a19fbeeb9606963915a0b4572017aea4eb377aa8 Mon Sep 17 00:00:00 2001
From: Ben Zickel <ben@vesttoo.com>
Date: Tue, 21 May 2024 14:45:01 +0300
Subject: [PATCH 17/24] Add iterations in order to assure convergence in
 parameter fit tests.

---
 tests/distributions/test_stable_with_log_prob.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tests/distributions/test_stable_with_log_prob.py b/tests/distributions/test_stable_with_log_prob.py
index e99354d2c3..ffc416386c 100644
--- a/tests/distributions/test_stable_with_log_prob.py
+++ b/tests/distributions/test_stable_with_log_prob.py
@@ -52,7 +52,7 @@ def model(data):
         with pyro.plate("data", data.shape[0]):
             pyro.sample("obs", Stable(alpha, beta, c, mu), obs=data)
 
-    def train(model, guide, num_steps=300, lr=0.03):
+    def train(model, guide, num_steps=400, lr=0.03):
         pyro.clear_param_store()
         pyro.set_rng_seed(20240520)
 
@@ -90,7 +90,7 @@ def log_progress():
 
 
 # # The below tests will be executed:
-# test_stable_with_log_prob_param_fit(1.00,   0.8,  2.0,  3.0, 1.3, 0.0, 1.0, 0.0)
+test_stable_with_log_prob_param_fit(1.00,   0.8,  2.0,  3.0, 1.3, 0.0, 1.0, 0.0)
 # test_stable_with_log_prob_param_fit(1.02,  -0.8,  2.0, -3.0, 1.3, 0.0, 1.0, 0.0)
 # test_stable_with_log_prob_param_fit(0.98,   0.5,  1.0, -3.0, 1.3, 0.0, 1.0, 0.0)
 # test_stable_with_log_prob_param_fit(0.95,  -0.5,  1.0,  3.0, 1.3, 0.0, 1.0, 0.0)

From a79eb7b187067ed8ef8e26b05dbd11481f0a5868 Mon Sep 17 00:00:00 2001
From: Ben Zickel <ben@vesttoo.com>
Date: Tue, 21 May 2024 14:53:15 +0300
Subject: [PATCH 18/24] Comment out test.

---
 tests/distributions/test_stable_with_log_prob.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/distributions/test_stable_with_log_prob.py b/tests/distributions/test_stable_with_log_prob.py
index ffc416386c..d7a5762c0b 100644
--- a/tests/distributions/test_stable_with_log_prob.py
+++ b/tests/distributions/test_stable_with_log_prob.py
@@ -90,7 +90,7 @@ def log_progress():
 
 
 # # The below tests will be executed:
-test_stable_with_log_prob_param_fit(1.00,   0.8,  2.0,  3.0, 1.3, 0.0, 1.0, 0.0)
+# test_stable_with_log_prob_param_fit(1.00, 0.8, 2.0, 3.0, 1.3, 0.0, 1.0, 0.0)
 # test_stable_with_log_prob_param_fit(1.02,  -0.8,  2.0, -3.0, 1.3, 0.0, 1.0, 0.0)
 # test_stable_with_log_prob_param_fit(0.98,   0.5,  1.0, -3.0, 1.3, 0.0, 1.0, 0.0)
 # test_stable_with_log_prob_param_fit(0.95,  -0.5,  1.0,  3.0, 1.3, 0.0, 1.0, 0.0)

From 3602f00f15d974fc0c8e2261c3249920c39ec1e7 Mon Sep 17 00:00:00 2001
From: Ben Zickel <ben@vesttoo.com>
Date: Tue, 21 May 2024 16:54:53 +0300
Subject: [PATCH 19/24] Increase test error limit.

---
 tests/distributions/test_stable_with_log_prob.py | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/tests/distributions/test_stable_with_log_prob.py b/tests/distributions/test_stable_with_log_prob.py
index d7a5762c0b..ed27ba0ff5 100644
--- a/tests/distributions/test_stable_with_log_prob.py
+++ b/tests/distributions/test_stable_with_log_prob.py
@@ -84,16 +84,16 @@ def log_progress():
 
     # Verify fit accuracy
     assert_close(alpha, pyro.param("alpha").item(), atol=0.03)
-    assert_close(beta, pyro.param("beta").item(), atol=0.04)
+    assert_close(beta, pyro.param("beta").item(), atol=0.06)
     assert_close(c, pyro.param("c").item(), atol=0.2)
     assert_close(mu, pyro.param("mu").item(), atol=0.2)
 
 
 # # The below tests will be executed:
-# test_stable_with_log_prob_param_fit(1.00, 0.8, 2.0, 3.0, 1.3, 0.0, 1.0, 0.0)
-# test_stable_with_log_prob_param_fit(1.02,  -0.8,  2.0, -3.0, 1.3, 0.0, 1.0, 0.0)
-# test_stable_with_log_prob_param_fit(0.98,   0.5,  1.0, -3.0, 1.3, 0.0, 1.0, 0.0)
-# test_stable_with_log_prob_param_fit(0.95,  -0.5,  1.0,  3.0, 1.3, 0.0, 1.0, 0.0)
-# test_stable_with_log_prob_param_fit(1.10,   0.0,  1.0,  0.0, 1.3, 0.0, 1.0, 0.0)
-# test_stable_with_log_prob_param_fit(1.80,  -0.5,  1.0, -2.0, 1.3, 0.0, 1.0, 0.0)
-# test_stable_with_log_prob_param_fit(0.50,   0.0,  1.0,  2.0, 1.3, 0.0, 1.0, 0.0)
+# test_stable_with_log_prob_param_fit(1.00,  0.8,  2.0,  3.0,  1.3,  0.0,  1.0,  0.0)
+# test_stable_with_log_prob_param_fit(1.02, -0.8,  2.0, -3.0,  1.3,  0.0,  1.0,  0.0)
+# test_stable_with_log_prob_param_fit(0.98,  0.5,  1.0, -3.0,  1.3,  0.0,  1.0,  0.0)
+# test_stable_with_log_prob_param_fit(0.95, -0.5,  1.0,  3.0,  1.3,  0.0,  1.0,  0.0)
+# test_stable_with_log_prob_param_fit(1.10,  0.0,  1.0,  0.0,  1.3,  0.0,  1.0,  0.0)
+# test_stable_with_log_prob_param_fit(1.80, -0.5,  1.0, -2.0,  1.3,  0.0,  1.0,  0.0)
+# test_stable_with_log_prob_param_fit(0.50,  0.0,  1.0,  2.0,  1.3,  0.0,  1.0,  0.0)

From 1ee43919dd1fd62d32f72a772e4b832ba9bda1c0 Mon Sep 17 00:00:00 2001
From: Ben Zickel <ben@vesttoo.com>
Date: Wed, 22 May 2024 09:18:09 +0300
Subject: [PATCH 20/24] Added StableWithLogProb docs.

---
 docs/source/distributions.rst |  7 +++++++
 pyro/distributions/stable.py  | 19 ++++++++++++++++++-
 2 files changed, 25 insertions(+), 1 deletion(-)

diff --git a/docs/source/distributions.rst b/docs/source/distributions.rst
index aee80f6cc4..2db4671660 100644
--- a/docs/source/distributions.rst
+++ b/docs/source/distributions.rst
@@ -407,6 +407,13 @@ Stable
     :undoc-members:
     :show-inheritance:
 
+StableWithLogProb
+-----------------
+.. autoclass:: pyro.distributions.StableWithLogProb
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
 TruncatedPolyaGamma
 -------------------
 .. autoclass:: pyro.distributions.TruncatedPolyaGamma
diff --git a/pyro/distributions/stable.py b/pyro/distributions/stable.py
index e508994f32..68f271bc34 100644
--- a/pyro/distributions/stable.py
+++ b/pyro/distributions/stable.py
@@ -208,4 +208,21 @@ def variance(self):
 
 
 class StableWithLogProb(StableLogProb, Stable):
-    pass
+    r"""
+    Levy :math:`\alpha`-stable distribution that is based on
+    :class:`Stable` but with an added method for calculating the
+    log probability density using numerical integration.
+
+    This should be used in cases where reparameterization does not work
+    like when trying to estimate the skew :math:`\beta` parameter. Running
+    times are slower than with reparameterization.
+
+    The numerical integration implementation is based on the algorithm
+    proposed by Chambers, Mallows and Stuck (CMS) for simulating the
+    Levy :math:`\alpha`-stable distribution. The CMS algorithm involves a
+    nonlinear transformation of two independent random variables into
+    one stable random variable. The first random variable is uniformly
+    distributed while the second is exponentially distributed. The numerical
+    integration is performed over the first uniformly distributed random
+    variable.
+    """

From ff8cd1f496251a28821c74833dedf3a653969995 Mon Sep 17 00:00:00 2001
From: Ben Zickel <ben@vesttoo.com>
Date: Mon, 27 May 2024 21:10:31 +0300
Subject: [PATCH 21/24] Cap near zero tolerance by inverse probability density.

---
 pyro/distributions/stable_log_prob.py | 37 ++++++++++++++++++++++-----
 1 file changed, 30 insertions(+), 7 deletions(-)

diff --git a/pyro/distributions/stable_log_prob.py b/pyro/distributions/stable_log_prob.py
index e71094ba73..7e53f7a327 100644
--- a/pyro/distributions/stable_log_prob.py
+++ b/pyro/distributions/stable_log_prob.py
@@ -6,7 +6,8 @@
 
 import torch
 
-value_near_zero_tolerance = 0.01
+value_near_zero_tolerance_alpha = 0.01
+value_near_zero_tolerance_density = 0.1
 alpha_near_one_tolerance = 0.05
 
 
@@ -108,23 +109,26 @@ def _unsafe_alpha_stable_log_prob_S0(alpha, beta, Z):
     Z = Z + beta * (math.pi / 2 * alpha).tan()
 
     # Find near zero values
-    per_alpha_value_near_zero_tolerance = (
-        value_near_zero_tolerance * alpha / (1 - alpha).abs()
+    per_param_value_near_zero_tolerance = (
+        value_near_zero_tolerance_alpha * alpha / (1 - alpha).abs()
+    ).clamp(
+        max=value_near_zero_tolerance_density
+        * _unsafe_alpha_stable_log_prob_at_zero(alpha, 0).exp().reciprocal()
     )
-    idx = Z.abs() < per_alpha_value_near_zero_tolerance
+    idx = Z.abs() < per_param_value_near_zero_tolerance
 
     # Calculate log-prob at safe values
     log_prob = _unsafe_stable_log_prob(
-        alpha, beta, torch.where(idx, per_alpha_value_near_zero_tolerance, Z)
+        alpha, beta, torch.where(idx, per_param_value_near_zero_tolerance, Z)
     )
 
     # Handle near zero values by interpolation
     if idx.any():
         log_prob_pos = log_prob[idx]
         log_prob_neg = _unsafe_stable_log_prob(
-            alpha[idx], beta[idx], -per_alpha_value_near_zero_tolerance[idx]
+            alpha[idx], beta[idx], -per_param_value_near_zero_tolerance[idx]
         )
-        weights = Z[idx] / (2 * per_alpha_value_near_zero_tolerance[idx]) + 0.5
+        weights = Z[idx] / (2 * per_param_value_near_zero_tolerance[idx]) + 0.5
         log_prob[idx] = torch.logsumexp(
             torch.stack(
                 (log_prob_pos + weights.log(), log_prob_neg + (1 - weights).log()),
@@ -192,3 +196,22 @@ def _unsafe_stable_given_uniform_log_prob(V, alpha, beta, Z):
     log_prob = log_prob.clamp(min=MIN_LOG, max=MAX_LOG)
 
     return log_prob
+
+
+def _unsafe_alpha_stable_log_prob_at_zero(alpha, beta):
+    # Calculate log-probability at value of zero. This will fail if alpha is close to 1
+    inv_alpha = alpha.reciprocal()
+    half_pi = math.pi / 2
+    ha = half_pi * alpha
+    b = beta * ha.tan()
+    atan_b = b.atan()
+
+    term1_log = (inv_alpha * atan_b).cos().log()
+    term2_log = atan_b.cos().log() * inv_alpha
+    term3_log = torch.lgamma(1 + inv_alpha)
+
+    log_prob = term1_log - term2_log + term3_log - math.log(math.pi)
+
+    log_prob = log_prob.clamp(min=MIN_LOG, max=MAX_LOG)
+
+    return log_prob

From 77d9c9f2c53d14ab53f1121f8fbca573ec7d6375 Mon Sep 17 00:00:00 2001
From: Ben Zickel <ben@vesttoo.com>
Date: Mon, 27 May 2024 22:37:23 +0300
Subject: [PATCH 22/24] Make log_prob return data type same as that of the
 input value.

---
 pyro/distributions/stable_log_prob.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/pyro/distributions/stable_log_prob.py b/pyro/distributions/stable_log_prob.py
index 7e53f7a327..b21f201019 100644
--- a/pyro/distributions/stable_log_prob.py
+++ b/pyro/distributions/stable_log_prob.py
@@ -53,13 +53,16 @@ class StableLogProb:
     def log_prob(self, value):
         # Undo shift and scale
         value = (value - self.loc) / self.scale
+        value_dtype = value.dtype
 
         # Use double precision math
         alpha = self.stability.double()
         beta = self.skew.double()
         value = value.double()
 
-        return _stable_log_prob(alpha, beta, value, self.coords) - self.scale.log()
+        log_prob = _stable_log_prob(alpha, beta, value, self.coords)
+
+        return log_prob.to(dtype=value_dtype) - self.scale.log()
 
 
 def _stable_log_prob(alpha, beta, value, coords):

From 9e8044cfad38f55b8835c34298c0d5703e26bb0a Mon Sep 17 00:00:00 2001
From: Ben Zickel <ben@vesttoo.com>
Date: Tue, 28 May 2024 00:05:46 +0300
Subject: [PATCH 23/24] Added Stable distirbution log-probability calculation
 goodness of fit tests.

---
 .../test_stable_with_log_prob.py              | 47 +++++++++++++++++++
 1 file changed, 47 insertions(+)

diff --git a/tests/distributions/test_stable_with_log_prob.py b/tests/distributions/test_stable_with_log_prob.py
index ed27ba0ff5..5b2cffadc0 100644
--- a/tests/distributions/test_stable_with_log_prob.py
+++ b/tests/distributions/test_stable_with_log_prob.py
@@ -5,17 +5,64 @@
 
 import pytest
 import torch
+from scipy.stats import levy_stable
 
 import pyro
+import pyro.distributions
+import pyro.distributions.stable_log_prob
 from pyro.distributions import StableWithLogProb as Stable
 from pyro.distributions import constraints
 from pyro.infer import SVI, Trace_ELBO
 from pyro.infer.autoguide import AutoNormal
 from tests.common import assert_close
+from tests.distributions.test_distributions import auto_goodness_of_fit
+
+TEST_FAILURE_RATE = 5e-4
+
 
 torch.set_default_dtype(torch.float64)
 
 
+@pytest.mark.parametrize("stability", [0.1, 0.95, 1.00, 1.05, 1.99])
+@pytest.mark.parametrize("skew", [-0.8, 0.0, 0.8])
+def test_stable_gof(stability, skew):
+    num_samples = 100000
+    # Use less samples for scipy as its log-probability calculation is much slower than pyro's
+    num_samples_scipy = 10000
+    pyro.set_rng_seed(20240527)
+
+    # Create distributions and samples
+    dist = Stable(stability, skew).expand(torch.Size([num_samples]))
+    dist_scipy = levy_stable(stability, skew)
+    dist_scipy.dist.parameterization = "S0"
+    samples = dist.sample()
+    samples_scipy = samples[:num_samples_scipy]
+
+    # Check goodness of fit of samples to scipy's implementation of the log-probability calculation.
+    logging.info(
+        f"Calculating log-probability of (stablity={stability}, "
+        "skew={skew}) for {len(samples_scipy)} samples with scipy"
+    )
+    probs_scipy = torch.Tensor(dist_scipy.pdf(samples_scipy))
+    gof_scipy = auto_goodness_of_fit(samples_scipy, probs_scipy)
+    assert gof_scipy > TEST_FAILURE_RATE
+    logging.info(
+        f"Goodness of fit failure rate is {gof_scipy} > {TEST_FAILURE_RATE} with scipy"
+    )
+
+    # Check goodness of fit of pyro's implementation of the log-probability calculation to generated samples.
+    logging.info(
+        f"Calculating log-probability of (stablity={stability}, "
+        "skew={skew}) for {len(samples)} samples with pyro"
+    )
+    probs = dist.log_prob(samples).exp()
+    gof = auto_goodness_of_fit(samples, probs)
+    assert gof > TEST_FAILURE_RATE
+    logging.info(
+        f"Goodness of fit failure rate is {gof} > {TEST_FAILURE_RATE} with pyro"
+    )
+
+
 @pytest.mark.parametrize(
     "alpha, beta, c, mu",
     [

From 06b4beca5e5fa44b4e468770d2604d89f00a26f1 Mon Sep 17 00:00:00 2001
From: Ben Zickel <ben@vesttoo.com>
Date: Tue, 28 May 2024 01:23:16 +0300
Subject: [PATCH 24/24] Added explanation of StableWithLogProb usage and
 results.

---
 tutorial/source/stable.ipynb | 357 ++++++++++++++++++++++++++++-------
 1 file changed, 285 insertions(+), 72 deletions(-)

diff --git a/tutorial/source/stable.ipynb b/tutorial/source/stable.ipynb
index 226ced505f..5650e8a6c1 100644
--- a/tutorial/source/stable.ipynb
+++ b/tutorial/source/stable.ipynb
@@ -8,7 +8,7 @@
     "\n",
     "This tutorial demonstrates inference using the Levy [Stable](http://docs.pyro.ai/en/stable/distributions.html#stable) distribution through a motivating example of a non-Gaussian stochastic volatilty model.\n",
     "\n",
-    "Inference with stable distribution is tricky because the density `Stable.log_prob()` is not defined. In this tutorial we demonstrate two approaches to inference: (i) using the [poutine.reparam](http://docs.pyro.ai/en/latest/poutine.html#pyro.poutine.handlers.reparam) effect to transform models in to a tractable form, and (ii) using the likelihood-free loss [EnergyDistance](http://docs.pyro.ai/en/latest/inference_algos.html#pyro.infer.energy_distance.EnergyDistance) with SVI.\n",
+    "Inference with stable distribution is tricky because the density `Stable.log_prob()` is not defined. In this tutorial we demonstrate two approaches to inference: (i) using the [poutine.reparam](http://docs.pyro.ai/en/latest/poutine.html#pyro.poutine.handlers.reparam) effect to transform models in to a tractable form, (ii) using the likelihood-free loss [EnergyDistance](http://docs.pyro.ai/en/latest/inference_algos.html#pyro.infer.energy_distance.EnergyDistance) with SVI, and (iii) using the `StableWithLogProb` distribution which has a numerically integrated log-probability calculation.\n",
     "\n",
     "\n",
     "#### Summary\n",
@@ -27,7 +27,9 @@
     "\n",
     "- [Daily S&P data](#data)\n",
     "- [Fitting a single distribution to log returns](#fitting) using `EnergyDistance`\n",
-    "- [Modeling stochastic volatility](#modeling) using `poutine.reparam`"
+    "- [Modeling stochastic volatility](#modeling) using:\n",
+    "    - [Reparameterization](#reparam) with `poutine.reparam`\n",
+    "    - [Numerically integrated log-probability](#numeric) of [StableWithLogProb](http://docs.pyro.ai/en/stable/distributions.html#stablewithlogprob)"
    ]
   },
   {
@@ -96,7 +98,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 900x300 with 1 Axes>"
       ]
@@ -128,7 +130,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 900x300 with 1 Axes>"
       ]
@@ -152,7 +154,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 900x300 with 1 Axes>"
       ]
@@ -204,28 +206,28 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "step 0 loss = 8.961664199829102\n",
-      "step 20 loss = 4.8506011962890625\n",
-      "step 40 loss = 1.5543489456176758\n",
-      "step 60 loss = 1.7787070274353027\n",
-      "step 80 loss = 1.4140945672988892\n",
-      "step 100 loss = 1.3671720027923584\n",
-      "step 120 loss = 1.287503719329834\n",
-      "step 140 loss = 1.2791334390640259\n",
-      "step 160 loss = 1.2810490131378174\n",
-      "step 180 loss = 1.2784368991851807\n",
-      "step 200 loss = 1.2823134660720825\n",
+      "step 0 loss = 7.497945785522461\n",
+      "step 20 loss = 2.0790653228759766\n",
+      "step 40 loss = 1.6773109436035156\n",
+      "step 60 loss = 1.4146158695220947\n",
+      "step 80 loss = 1.306936502456665\n",
+      "step 100 loss = 1.2835698127746582\n",
+      "step 120 loss = 1.2812254428863525\n",
+      "step 140 loss = 1.2803162336349487\n",
+      "step 160 loss = 1.2787212133407593\n",
+      "step 180 loss = 1.265405535697937\n",
+      "step 200 loss = 1.2878881692886353\n",
       "--------------------\n",
-      "loc = 0.0003696\n",
-      "scale = 0.00872\n",
-      "stability = 1.977\n",
-      "CPU times: user 15.6 s, sys: 521 ms, total: 16.1 s\n",
-      "Wall time: 2.38 s\n"
+      "loc = 0.0002415\n",
+      "scale = 0.008325\n",
+      "stability = 1.982\n",
+      "CPU times: total: 828 ms\n",
+      "Wall time: 2.93 s\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 900x300 with 1 Axes>"
       ]
@@ -266,7 +268,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 900x300 with 1 Axes>"
       ]
@@ -335,7 +337,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def model(data):\n",
+    "def model(data, r_dist=dist.Stable):\n",
     "    # Note we avoid plates because we'll later reparameterize along the time axis using\n",
     "    # DiscreteCosineReparam, breaking independence. This requires .unsqueeze()ing scalars.\n",
     "    h_0 = pyro.sample(\"h_0\", dist.Normal(0, 1)).unsqueeze(-1)\n",
@@ -348,7 +350,7 @@
     "    r_loc = pyro.sample(\"r_loc\", dist.Normal(0, 1e-2)).unsqueeze(-1)\n",
     "    r_skew = pyro.sample(\"r_skew\", dist.Uniform(-1, 1)).unsqueeze(-1)\n",
     "    r_stability = pyro.sample(\"r_stability\", dist.Uniform(0, 2)).unsqueeze(-1)\n",
-    "    pyro.sample(\"r\", dist.Stable(r_stability, r_skew, sqrt_h, r_loc * sqrt_h).to_event(1),\n",
+    "    pyro.sample(\"r\", r_dist(r_stability, r_skew, sqrt_h, r_loc * sqrt_h).to_event(1),\n",
     "                obs=data)"
    ]
   },
@@ -356,6 +358,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "### Fitting a Model with Reparameterization <a class=\"anchor\" id=\"reparam\"></a>\n",
+    "\n",
     "We use two reparameterizers: [StableReparam](http://docs.pyro.ai/en/latest/infer.reparam.html#pyro.infer.reparam.stable.StableReparam) to handle the `Stable` likelihood (since `Stable.log_prob()` is undefined), and [DiscreteCosineReparam](http://docs.pyro.ai/en/latest/infer.reparam.html#pyro.infer.reparam.discrete_cosine.DiscreteCosineReparam) to improve geometry of the latent Gaussian process for `v`. We'll then use `reparam_model` for both inference and prediction."
    ]
   },
@@ -378,40 +382,45 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "step 0 loss = 80.7915\n",
-      "step 50 loss = 2.49764\n",
-      "step 100 loss = 6.18623\n",
-      "step 150 loss = -1.42891\n",
-      "step 200 loss = -2.48601\n",
-      "step 250 loss = -2.75234\n",
-      "step 300 loss = -2.80716\n",
-      "step 350 loss = -2.64854\n",
-      "step 400 loss = -2.93349\n",
-      "step 450 loss = -2.90964\n",
-      "step 500 loss = -2.93564\n",
-      "step 550 loss = -2.98376\n",
-      "step 600 loss = -3.01648\n",
-      "step 650 loss = -3.01208\n",
-      "step 700 loss = -3.04329\n",
-      "step 750 loss = -3.03045\n",
-      "step 800 loss = -3.04258\n",
-      "step 850 loss = -3.06856\n",
-      "step 900 loss = -3.05272\n",
-      "step 950 loss = -3.06414\n",
-      "step 1000 loss = -3.06487\n",
+      "step 0 loss = 2244.54\n",
+      "step 200 loss = -1.16091\n",
+      "step 400 loss = -2.96091\n",
+      "step 600 loss = -3.01823\n",
+      "step 800 loss = -3.03623\n",
+      "step 1000 loss = -3.04261\n",
+      "step 1200 loss = -3.07324\n",
+      "step 1400 loss = -3.06965\n",
+      "step 1600 loss = -3.08399\n",
+      "step 1800 loss = -3.08298\n",
+      "step 2000 loss = -3.08325\n",
+      "step 2200 loss = -3.09142\n",
+      "step 2400 loss = -3.09739\n",
+      "step 2600 loss = -3.10487\n",
+      "step 2800 loss = -3.09952\n",
+      "step 3000 loss = -3.10444\n",
       "--------------------\n",
-      "h_0 = 0.3713 ± 0.01079\n",
-      "r_loc = 0.05134 ± 0.002976\n",
-      "r_skew = 0.0001597 ± 0.0002002\n",
-      "r_stability = 1.92 ± 0.001772\n",
-      "sigma = 0.2373 ± 0.000313\n",
-      "CPU times: user 38.1 s, sys: 6.95 s, total: 45.1 s\n",
-      "Wall time: 45.1 s\n"
+      "h_0 = -0.2587 ± 0.00434\n",
+      "r_loc = 0.04707 ± 0.002965\n",
+      "r_skew = 0.001134 ± 0.0001323\n",
+      "r_stability = 1.946 ± 0.001327\n",
+      "sigma = 0.1359 ± 6.603e-05\n",
+      "CPU times: total: 19.7 s\n",
+      "Wall time: 2min 54s\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "text/plain": [
+       "(-3.119090303321589, 20.0)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
       "text/plain": [
        "<Figure size 900x300 with 1 Axes>"
       ]
@@ -424,20 +433,28 @@
     "%%time\n",
     "pyro.clear_param_store()\n",
     "pyro.set_rng_seed(1234567890)\n",
-    "num_steps = 1 if smoke_test else 1001\n",
-    "optim = ClippedAdam({\"lr\": 0.05, \"betas\": (0.9, 0.99), \"lrd\": 0.1 ** (1 / num_steps)})\n",
-    "guide = AutoDiagonalNormal(reparam_model)\n",
-    "svi = SVI(reparam_model, guide, optim, Trace_ELBO())\n",
-    "losses = []\n",
-    "for step in range(num_steps):\n",
-    "    loss = svi.step(r) / len(r)\n",
-    "    losses.append(loss)\n",
-    "    if step % 50 == 0:\n",
-    "        median = guide.median()\n",
-    "        print(\"step {} loss = {:0.6g}\".format(step, loss))\n",
+    "\n",
+    "def fit_model(model):\n",
+    "    num_steps = 1 if smoke_test else 3001\n",
+    "    optim = ClippedAdam({\"lr\": 0.05, \"betas\": (0.9, 0.99), \"lrd\": 0.1 ** (1 / num_steps)})\n",
+    "    guide = AutoDiagonalNormal(model)\n",
+    "    svi = SVI(model, guide, optim, Trace_ELBO())\n",
+    "    losses = []\n",
+    "    stats = []\n",
+    "    for step in range(num_steps):\n",
+    "        loss = svi.step(r) / len(r)\n",
+    "        losses.append(loss)\n",
+    "        stats.append(guide.quantiles([0.325, 0.675]).items())\n",
+    "        if step % 200 == 0:\n",
+    "            median = guide.median()\n",
+    "            print(\"step {} loss = {:0.6g}\".format(step, loss))\n",
+    "\n",
+    "    return guide, losses, stats\n",
+    "\n",
+    "guide, losses, stats = fit_model(reparam_model)\n",
     "\n",
     "print(\"-\" * 20)\n",
-    "for name, (lb, ub) in sorted(guide.quantiles([0.325, 0.675]).items()):\n",
+    "for name, (lb, ub) in sorted(stats[-1]):\n",
     "    if lb.numel() == 1:\n",
     "        lb = lb.squeeze().item()\n",
     "        ub = ub.squeeze().item()\n",
@@ -448,7 +465,7 @@
     "pyplot.ylabel(\"loss\")\n",
     "pyplot.xlabel(\"SVI step\")\n",
     "pyplot.xlim(0, len(losses))\n",
-    "pyplot.ylim(min(losses), 20)"
+    "pyplot.ylim(min(losses), 20)\n"
    ]
   },
   {
@@ -465,7 +482,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 900x500 with 2 Axes>"
       ]
@@ -482,13 +499,14 @@
     "axes[1].set_xlim(0, len(r))\n",
     "\n",
     "# We will pull out median log returns using the autoguide's .median() and poutines.\n",
+    "num_samples = 200\n",
     "with torch.no_grad():\n",
-    "    pred = Predictive(reparam_model, guide=guide, num_samples=20, parallel=True)(r)\n",
+    "    pred = Predictive(reparam_model, guide=guide, num_samples=num_samples, parallel=True)(r)\n",
     "log_h = pred[\"log_h\"]\n",
     "axes[0].plot(log_h.median(0).values, lw=1)\n",
     "axes[0].fill_between(torch.arange(len(log_h[0])),\n",
-    "                     log_h.kthvalue(2, dim=0).values,\n",
-    "                     log_h.kthvalue(18, dim=0).values,\n",
+    "                     log_h.kthvalue(int(num_samples * 0.1), dim=0).values,\n",
+    "                     log_h.kthvalue(int(num_samples * 0.9), dim=0).values,\n",
     "                     color='red', alpha=0.5)\n",
     "axes[0].set_ylabel(\"log volatility\")\n",
     "\n",
@@ -503,6 +521,201 @@
    "source": [
     "Observe that volatility roughly follows areas of large absolute log returns. Note that the uncertainty is underestimated, since we have used an approximate `AutoDiagonalNormal` guide. For more precise uncertainty estimates, one could use [HMC](http://docs.pyro.ai/en/stable/mcmc.html#hmc) or [NUTS](http://docs.pyro.ai/en/stable/mcmc.html#nuts) inference."
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Fitting a Model with Numerically Integrated Log-Probability <a class=\"anchor\" id=\"numeric\"></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We now create a model with the `Stable` distirbution replaced by `StableWithLogProb` which has a method for calculating the log-probability density."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from functools import partial\n",
+    "model_with_log_prob = poutine.reparam(partial(model, r_dist=dist.StableWithLogProb), {\"v\": DiscreteCosineReparam()})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "step 0 loss = 10.872\n",
+      "step 200 loss = -3.21741\n",
+      "step 400 loss = -3.28172\n",
+      "step 600 loss = -3.28264\n",
+      "step 800 loss = -3.28722\n",
+      "step 1000 loss = -3.29258\n",
+      "step 1200 loss = -3.28663\n",
+      "step 1400 loss = -3.30035\n",
+      "step 1600 loss = -3.29928\n",
+      "step 1800 loss = -3.30102\n",
+      "step 2000 loss = -3.30336\n",
+      "step 2200 loss = -3.30392\n",
+      "step 2400 loss = -3.30549\n",
+      "step 2600 loss = -3.30622\n",
+      "step 2800 loss = -3.30624\n",
+      "step 3000 loss = -3.30575\n",
+      "--------------------\n",
+      "h_0 = -0.2038 ± 0.005494\n",
+      "r_loc = 0.04509 ± 0.003355\n",
+      "r_skew = -0.09735 ± 0.02454\n",
+      "r_stability = 1.918 ± 0.002963\n",
+      "sigma = 0.1391 ± 6.794e-05\n",
+      "CPU times: total: 1h 20min 43s\n",
+      "Wall time: 1h 7s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(-3.3079991399111877, 20.0)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "pyro.clear_param_store()\n",
+    "pyro.set_rng_seed(1234567890)\n",
+    "\n",
+    "guide_with_log_prob, losses_with_log_prob, stats_with_log_prob = fit_model(model_with_log_prob)\n",
+    "\n",
+    "print(\"-\" * 20)\n",
+    "for name, (lb, ub) in sorted(stats_with_log_prob[-1]):\n",
+    "    if lb.numel() == 1:\n",
+    "        lb = lb.squeeze().item()\n",
+    "        ub = ub.squeeze().item()\n",
+    "        print(\"{} = {:0.4g} ± {:0.4g}\".format(name, (lb + ub) / 2, (ub - lb) / 2))\n",
+    "\n",
+    "pyplot.figure(figsize=(9, 3))\n",
+    "pyplot.plot(losses_with_log_prob)\n",
+    "pyplot.ylabel(\"loss\")\n",
+    "pyplot.xlabel(\"SVI step\")\n",
+    "pyplot.xlim(0, len(losses_with_log_prob))\n",
+    "pyplot.ylim(min(losses_with_log_prob), 20)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The log returns exhibit a negative skew which was not captured by the model with reparameterization of the `Stable` distribution. The negative skew means that negative log returns have a heavier tail than the tail of positive log returns. Also the stability parameter is slightly lower than the one found using the model with reparameterization of the `Stable` distribution (lower stability means heavier tails).\n",
+    "\n",
+    "Comparing convergence of the two models (see below graphs) we can see that without `Stable` distribution reparameterization less iterations are required for the stability parameter to converge, but since per iteration running times without `Stable` distribution reparameterization is much higher, the overall running time without `Stable` distribution reparameterization is significantly higher.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stability_with_log_prob = []\n",
+    "skew_with_log_prob = []\n",
+    "for stat in stats_with_log_prob:\n",
+    "    stat = dict(stat)\n",
+    "    stability_with_log_prob.append(stat['r_stability'].mean().item())\n",
+    "    skew_with_log_prob.append(stat['r_skew'].mean().item())\n",
+    "\n",
+    "stability = []\n",
+    "skew = []\n",
+    "for stat in stats:\n",
+    "    stat = dict(stat)\n",
+    "    stability.append(stat['r_stability'].mean().item())\n",
+    "    skew.append(stat['r_skew'].mean().item())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1.8, 2.0)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_comparison(log_prob_values, reparam_values, xlabel, ylabel):\n",
+    "    pyplot.plot(log_prob_values, color='b', label='Log-Prob')\n",
+    "    pyplot.plot(reparam_values, color='r', label='Reparam')\n",
+    "    pyplot.xlabel(xlabel)\n",
+    "    pyplot.ylabel(ylabel)\n",
+    "    pyplot.legend(loc='best')\n",
+    "    pyplot.grid()\n",
+    "\n",
+    "pyplot.subplot(2,1,1)\n",
+    "plot_comparison(stability_with_log_prob, stability, '', 'Stability')\n",
+    "\n",
+    "pyplot.subplot(2,1,2)\n",
+    "plot_comparison(stability_with_log_prob, stability, 'SVI step', 'Stability (Zoomed)')\n",
+    "pyplot.ylim(1.8, 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_comparison(skew_with_log_prob, skew, 'SVI step', 'Skew')"
+   ]
   }
  ],
  "metadata": {
@@ -521,7 +734,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.0"
+   "version": "3.9.18"
   }
  },
  "nbformat": 4,