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Bayesian Comparison of Latent Variable Models: Conditional Versus Marginal Likelihoods

Published online by Cambridge University Press:  01 January 2025

Edgar C. Merkle Open the ORCID record for Edgar C. Merkle [Opens in a new window] ,
Daniel Furr  and
Sophia Rabe-Hesketh
Edgar C. Merkle*
Affiliation:
University of Missouri
Daniel Furr
Affiliation:
University of California, Berkeley
Sophia Rabe-Hesketh
Affiliation:
University of California, Berkeley
*
Correspondence should be made to Edgar C. Merkle, University of Missouri, Columbia, MO, USA.Email: merklee@missouri.edu
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Abstract

Typical Bayesian methods for models with latent variables (or random effects) involve directly sampling the latent variables along with the model parameters. In high-level software code for model definitions (using, e.g., BUGS, JAGS, Stan), the likelihood is therefore specified as conditional on the latent variables. This can lead researchers to perform model comparisons via conditional likelihoods, where the latent variables are considered model parameters. In other settings, however, typical model comparisons involve marginal likelihoods where the latent variables are integrated out. This distinction is often overlooked despite the fact that it can have a large impact on the comparisons of interest. In this paper, we clarify and illustrate these issues, focusing on the comparison of conditional and marginal Deviance Information Criteria (DICs) and Watanabe–Akaike Information Criteria (WAICs) in psychometric modeling. The conditional/marginal distinction corresponds to whether the model should be predictive for the clusters that are in the data or for new clusters (where “clusters” typically correspond to higher-level units like people or schools). Correspondingly, we show that marginal WAIC corresponds to leave-one-cluster out cross-validation, whereas conditional WAIC corresponds to leave-one-unit out. These results lead to recommendations on the general application of the criteria to models with latent variables.

Keywords

Bayesian information criteriaconditional likelihoodcross-validationDICIRTleave-one-cluster outmarginal likelihoodMCMCSEMWAIC

Information

Type
Original Paper
Information
Psychometrika , Volume 84 , Issue 3 , September 2019 , pp. 802 - 829
Copyright
Copyright © 2019 The Psychometric Society

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Footnotes

Electronic supplementary material The online version of this article (https://doi.org/10.1007/s11336-019-09679-0) contains supplementary material, which is available to authorized users.

The R code and the real MGRM item parameters used in this paper are available online.

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