A SAS macro for automated stopping of Markov chain Monte Carlo estimation in Bayesian modeling with PROC MCMC
Publication date
2023-09-05
Document type
Forschungsartikel
Author
Organisational unit
Publisher
MDPI
Series or journal
Psych
ISSN
Periodical volume
5
Periodical issue
3
First page
966
Last page
982
Peer-reviewed
✅
Part of the university bibliography
✅
Language
English
Abstract
A crucial challenge in Bayesian modeling using Markov chain Monte Carlo (MCMC) estimation is to diagnose the convergence of the chains so that the draws can be expected to closely approximate the posterior distribution on which inference is based. A close approximation guarantees that the MCMC error exhibits only a negligible impact on model estimates and inferences. However, determining whether convergence has been achieved can often be challenging and cumbersome when relying solely on inspecting the trace plots of the chain(s) or manually checking the stopping criteria. In this article, we present a SAS macro called %automcmc that is based on PROC MCMC and that automatically continues to add draws until a user-specified stopping criterion (i.e., a certain potential scale reduction and/or a certain effective sample size) is reached for the chain(s).
Version
Published version
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Metadata only access