Optimal thinning of MCMC output

Marina Riabiz, Wilson Ye Chen, Jon Cockayne, Pawel Swietach, Steven Niederer, Lester Mackey, Chris Oates*

*Corresponding author for this work

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The use of heuristics to assess the convergence and compress the output of Markov
chain Monte Carlo can be sub-optimal in terms of the empirical approximations that
are produced. Typically a number of the initial states are attributed to “burn in” and
removed, whilst the remainder of the chain is “thinned” if compression is also required.
In this paper we consider the problem of retrospectively selecting a subset of states,
of fixed cardinality, from the sample path such that the approximation provided by
their empirical distribution is close to optimal. A novel method is proposed, based on
greedy minimisation of a kernel Stein discrepancy, that is suitable when the gradient
of the log-target can be evaluated and approximation using a small number of states is required. Theoretical results guarantee consistency of the method and its effectiveness is demonstrated in the challenging context of parameter inference for ordinary differential equations. Software is available in the Stein Thinning package in Python, R and MATLAB.
Original languageEnglish
JournalJournal of the Royal Statistical Society. Series B: Statistical Methodology
Publication statusAccepted/In press - 11 Jul 2021


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