Moved MCMCSummary to diagnostics in docs. Closes #1281

Author: MichaelClerx

docs/source/diagnostic_plots.rst

@@ -7,31 +7,31 @@ Diagnostic plots
 For users who have Matplotlib installed, Pints offers a number of diagnostic
 plots that can be used to quickly check obtained results.
 
-Plotting functions:
+Plots of functions:
 
 - :func:`function`
 - :func:`function_between_points`
 - :func:`surface`
 
-Diagnosing MCMC results:
+MCMC Diagnostic plots:
 
 - :func:`autocorrelation`
 - :func:`histogram`
 - :func:`pairwise`
 - :func:`series`
 - :func:`trace`
 
-Functions
----------
+Plots of functions
+------------------
 
 .. autofunction:: function
 
 .. autofunction:: function_between_points
 
 .. autofunction:: surface
 
-MCMC Diagnostics
-----------------
+MCMC Diagnostic plots
+---------------------
 
 .. autofunction:: autocorrelation
 

docs/source/index.rst

@@ -146,7 +146,6 @@ Contents
     abc_samplers/index
     boundaries
     core_classes_and_methods
-    diagnostics
     diagnostic_plots
     error_measures
     function_evaluation
@@ -155,6 +154,7 @@ Contents
     log_pdfs
     log_priors
     mcmc_samplers/index
+    mcmc_diagnostics
     nested_samplers/index
     noise_generators
     optimisers/index

docs/source/diagnostics.rst docs/source/mcmc_diagnostics.rstdocs/source/diagnostics.rst renamed to docs/source/mcmc_diagnostics.rst

@@ -1,11 +1,12 @@
-***********************
-Diagnosing MCMC results
-***********************
+****************
+MCMC Diagnostics
+****************
 
 .. currentmodule:: pints
 
-Pints provides a number of functions to diagnose MCMC progress and convergence.
+PINTS provides a number of functions to diagnose MCMC progress and convergence.
 
+.. autoclass:: MCMCSummary
 
 .. autofunction:: rhat
 

docs/source/mcmc_samplers/index.rst

@@ -33,4 +33,4 @@ interface, that can be used to sample from an unknown
     slice_doubling_mcmc
     slice_rank_shrinking_mcmc
     slice_stepout_mcmc
-    summary_mcmc
+

docs/source/mcmc_samplers/summary_mcmc.rst

@@ -133,7 +133,7 @@ def _between(chains):
 def rhat(chains, warm_up=0.0):
     r"""
     Returns the convergence measure :math:`\hat{R}` for the approximate
-    posterior according to [1]_.
+    posterior according to [3]_.
 
     :math:`\hat{R}` diagnoses convergence by checking mixing and stationarity
     of :math:`m` chains (at least two, :math:`m\geq 2`). To diminish the
@@ -180,8 +180,8 @@ def rhat(chains, warm_up=0.0):
 
     References
     ----------
-    ..  [1] "Bayesian data analysis", ch. 11.4 'Inference and assessing
-        convergence', 3rd edition, Gelman et al., 2014.
+    .. [3] "Bayesian data analysis", ch. 11.4 'Inference and assessing
+           convergence', 3rd edition, Gelman et al., 2014.
 
     Parameters
     ----------

pints/_diagnostics.py

@@ -72,7 +72,6 @@ class MonomialGammaHamiltonianMCMC(pints.SingleChainMCMC):
            Lawrence Cari.
            Advances in Neural Information Processing Systems (NIPS)
 
-
     .. [2] MCMC using Hamiltonian dynamics
            Radford M. Neal, Chapter 5 of the Handbook of Markov Chain Monte
            Carlo by Steve Brooks, Andrew Gelman, Galin Jones, and Xiao-Li Meng.