DOC Add advi-minibatch, diagnostics and backends to docs. Fix some do…

…c string math problems.

docs/source/api.rst

@@ -71,6 +71,17 @@ Multivariate
 .. automodule:: pymc3.distributions.multivariate
    :members:
 
+Mixture
+^^^^^^^
+
+.. currentmodule:: pymc3.distributions.mixture
+.. autosummary::
+   Mixture
+   NormalMixture
+
+.. automodule:: pymc3.distributions.mixture
+   :members:
+
 Plots
 -----
 
@@ -89,6 +100,16 @@ Stats
 .. automodule:: pymc3.stats
    :members:
 
+Diagnostics
+-----------
+
+.. currentmodule:: pymc3.diagnostics
+   :members:
+
+.. automodule:: pymc3.diagnostics
+   :members:
+
+
 
 Inference
 ---------
@@ -142,14 +163,6 @@ Hamiltonian Monte Carlo
 .. automodule:: pymc3.step_methods.hmc
    :members:
 
-ATMCMC
-""""""
-
-.. currentmodule:: pymc3.step_methods.ATMCMC
-   :members:
-
-.. automodule:: pymc3.step_methods.ATMCMC
-   :members:
 
 Variational
 ^^^^^^^^^^^
@@ -162,3 +175,26 @@ ADVI
 
 .. automodule:: pymc3.variational.advi
    :members:
+
+ADVI minibatch
+""""""""""""""
+
+.. currentmodule:: pymc3.variational.advi_minibatch
+   :members:
+
+.. automodule:: pymc3.variational.advi_minibatch
+   :members:
+
+Backends
+--------
+
+.. currentmodule:: pymc3.backends
+   :members:
+
+.. automodule:: pymc3.backends
+   :members:
+
+
+
+
+

pymc3/diagnostics.py

@@ -8,7 +8,7 @@
 
 @statfunc
 def geweke(x, first=.1, last=.5, intervals=20):
-    """Return z-scores for convergence diagnostics.
+    R"""Return z-scores for convergence diagnostics.
 
     Compare the mean of the first % of series with the mean of the last % of
     series. x is divided into a number of segments for which this difference is
@@ -96,7 +96,7 @@ def geweke(x, first=.1, last=.5, intervals=20):
 
 
 def gelman_rubin(mtrace):
-    """ Returns estimate of R for a set of traces.
+    R"""Returns estimate of R for a set of traces.
 
     The Gelman-Rubin diagnostic tests for lack of convergence by comparing
     the variance between multiple chains to the variance within each chain.
@@ -160,25 +160,24 @@ def gelman_rubin(mtrace):
 
 
 def effective_n(mtrace):
-    """ Returns estimate of the effective sample size of a set of traces.
+    R"""Returns estimate of the effective sample size of a set of traces.
 
     Parameters
     ----------
     mtrace : MultiTrace
-      A MultiTrace object containing parallel traces (minimum 2)
-      of one or more stochastic parameters.
+        A MultiTrace object containing parallel traces (minimum 2)
+        of one or more stochastic parameters.
 
     Returns
     -------
     n_eff : float
-      Return the effective sample size, :math:`\hat{n}_{eff}`
+        Return the effective sample size, :math:`\hat{n}_{eff}`
 
     Notes
     -----
-
     The diagnostic is computed by:
 
-      .. math:: \hat{n}_{eff} = \frac{mn}}{1 + 2 \sum_{t=1}^T \hat{\rho}_t}
+    .. math:: \hat{n}_{eff} = \frac{mn}{1 + 2 \sum_{t=1}^T \hat{\rho}_t}
 
     where :math:`\hat{\rho}_t` is the estimated autocorrelation at lag t, and T
     is the first odd positive integer for which the sum

pymc3/distributions/mixture.py

@@ -142,7 +142,7 @@ class NormalMixture(Mixture):
     R"""
     Normal mixture log-likelihood
 
-    .. math:: f(x \mid w, \mu, \sigma^2) = \sum_{i = 1}^n w_i N(x \mid \mu_i, \sigma^2_i
+    .. math:: f(x \mid w, \mu, \sigma^2) = \sum_{i = 1}^n w_i N(x \mid \mu_i, \sigma^2_i)
 
     ========  =======================================
     Support   :math:`x \in \mathbb{R}`