Commit 166af1a1 authored by Gregory Ashton's avatar Gregory Ashton
Browse files

Adds simple convergence stat. example

parent 4be56f25
......@@ -464,3 +464,33 @@ year = {2015}
year = {2011}
title={Markov chain Monte Carlo convergence diagnostics: a comparative review},
author={Cowles, Mary Kathryn and Carlin, Bradley P},
journal={Journal of the American Statistical Association},
publisher={Taylor \& Francis}
title={Inference from iterative simulation using multiple sequences},
author={Gelman, Andrew and Rubin, Donald B},
journal={Statistical science},
title={General methods for monitoring convergence of iterative simulations},
author={Brooks, Stephen P and Gelman, Andrew},
journal={Journal of computational and graphical statistics},
publisher={Taylor \& Francis}
......@@ -384,6 +384,7 @@ such, this sampler requires little in the way of tuning: a single proposal
scale and the number of steps to take.
\subsection{Parallel tempering: sampling multimodal posteriors}
Beyond the standard ensemble sampler, we will also use one further
modification, the parallel-tempered ensemble sampler. A parallel
tempered MCMC simulation, first proposed by \citet{swendsen1986}, runs
......@@ -751,6 +752,89 @@ $\widetilde{2\F}$ taken from the production samples.}
\section{Testing convergence}
In Figure~\ref{fig_MCMC_simple_example}, it is easy to see by eye that that the
MCMC simulation has converged during the burn-in. However, choosing the number
of steps, balancing the extra computing cost against ensuring the chains have
converged, is often a matter of trial and error. So, while graphical methods
for assesing convergence are the most robust and informative, quantitative
methods (diagnostics) also exist to test convergence and can therefore provide
the utility to prematurely stop the burn-in period. As such one can then simply
choose a large number of steps (as compared to a typical burn-in for the given
problem) and expect that in most cases these will not all be used. Moreover, if
such a large number of searches are to be run such that inspection of each MCMC
simulation is impossible, the convergence tests can easily be checked
\subsection{The Gelman \& Rubin statistic}
We will use the ``Gelman \& Rubin statistic" \citep{gelman1992} to assess
convergence. This method, developed in the context of single-walker samplers
(i.e. not ensemble samplers) requires multiple independent simulations to be
run, each initialised from an overdispersed estimate of the target
distribution. The statistic, the \emph{potential scale reduction factor}
(PSRF), is calculated for each scalar quantity of interest and estimates the
amount by which the scale factor of the target distribition (approximated by a
Student t distribution) might shrink if the simulation where run indefinitely.
This method makes a normality assumption on the posterior distribution, which
in testing appears to be reasonble for all parameters except the transient
start time and durations (discussed later in Sec.~\ref{sec_transients}).
However, paraphrasing \citet{brooks1998}, Gelman and Rubin's approach is
generally applicable to any situation where the posterior inference can be
summarized by the mean and variance (or standard deviation), even if the
posterior distributions are not themselves believed to be normally distributed.
\comment{Note I have not yet fully implemented the method, for simplicity I'm
assuming df -> infty}
In this setting, we depart from the original description by using the walkers
of the ensemble sampler in place of independent MCMC simulations. Moreover,
this is done periodicically checking the last $n$ steps of the simulation. As
such, testing convergence requires only a small overhead in calculating the
variances: there is no requirement to run multiple independent simulations.
At each periodic check, the statistic is computed for each model parameter
and if all model parameters are less than the threshold (with a default value
of 1.2), the simulation is considered converged. However, the burn-in period is
only prematurely stopped if convergence is reached a pre-specific number of
times (the default value is 10). This is a conservative system to prevent
misdiagnosis of convergence.
While in testing this method was found to agree well with graphical
determination convergence, but we echo the conclusions of \citet{cowles1996markov}
that no convergence diagnostic can say with certainty that the posterior
samples are truly representative of the underlying stationary distribution.
In Figure~\ref{fig_MCMC_convergence_example}, we illustrate our implementation
of the Rubin-Gelman statistic for a fully-coherent search of data including
a strong continuous signal. In essense this repeats the simulation in
Figure~\ref{fig_MCMC_simple_example}, but we have expanded the prior ranges
on $f$ and $\fdot$. As a result, the sampler takes many more steps to reach
convergence: it can be seen by eye that the traces of a few walkers remain
isolated from the bulk of walkers. When this is the case, the PSRF is much
larger than unity, since the between-walker variance is much larger than the
within-walker variance. Once that walker joins the main bulk, the PSRF tends
to unity for several periods and hence the burn-in period is prematurely
halted. The sampler is then continued, with all samples saved as production
samples. In the figure, the gap indicates the `missing' samples which would
have been taken if convergence had not been obtained before the end of the
maximum burn-in period.
\caption{Demonstration of the Potential Scale Reduction Face (PSRF) convergence
statistic (in blue) calculated separately for each parameter: values below 1.2
are considered converged.}
It is worth noting that this type of long convergence, when a small number of
chains remain isolated from the main bulk can be easily countered by the use
of parallel tempering (see Sec.~\ref{sec_parallel_tempering}). This was intenionally
not used here to highlight the utility of the convergence statistic.
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