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R*: A robust MCMC convergence diagnostic with uncertainty using decision tree classifiers

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Title: R*: A robust MCMC convergence diagnostic with uncertainty using decision tree classifiers
Authors: Lambert, B
Vehtari, A
Item Type: Journal Article
Abstract: Markov chain Monte Carlo (MCMC) has transformed Bayesian model inference over the past three decades: mainly because of this, Bayesian inference is now a workhorse of applied scientists. Under general conditions, MCMC sampling converges asymptotically to the posterior distribution, but this provides no guarantees about its performance in finite time. The predominant method for monitoring convergence is to run multiple chains and monitor individual chains’ characteristics and compare these to the population as a whole: if within-chain and between-chain summaries are comparable, then this is taken to indicate that the chains have converged to a common stationary distribution. Here, we introduce a new method for diagnosing convergence based on how well a machine learning classifier model can successfully discriminate the individual chains. We call this convergence measure R∗. In contrast to the predominant̂ R, R∗ is a single statistic across all parameters that indicates lack of mixing, although individual variables’ importance for this metric can also be determined. Additionally ,R∗ is not based on any single characteristic of the sampling distribution; instead it uses all the information in the chain, including that given by the joint sampling distribution, which is currently largely overlooked by existing approaches. We recommend calculating R∗ using two different machine learning classifiers — gradient-boosted regression trees and random forests —which each work well in models of different dimensions. Because each of these methods outputs a classification probability, as a by product, we obtain uncertainty in R∗. The method is straight forward to implement and could be a complementary additional check on MCMC convergence for applied analyses.
Date of Acceptance: 17-Nov-2020
URI: http://hdl.handle.net/10044/1/85769
ISSN: 1931-6690
Publisher: International Society for Bayesian Analysis (ISBA)
Journal / Book Title: Bayesian Analysis
Copyright Statement: This paper is embargoed until publication. Once published it will be available fully open access.
Keywords: 0104 Statistics
Statistics & Probability
Publication Status: Accepted
Embargo Date: publication subject to indefinite embargo
Appears in Collections:School of Public Health