A Simple Method for Comparing Complex Models: Bayesian Model Comparison for Hierarchical Multinomial Processing Tree Models Using Warp-III Bridge Sampling

Quentin F. Gronau (), Eric-Jan Wagenmakers, Daniel W. Heck and Dora Matzke
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Quentin F. Gronau: University of Amsterdam
Eric-Jan Wagenmakers: University of Amsterdam
Daniel W. Heck: University of Mannheim
Dora Matzke: University of Amsterdam

Psychometrika, 2019, vol. 84, issue 1, No 13, 284 pages

Abstract: Abstract Multinomial processing trees (MPTs) are a popular class of cognitive models for categorical data. Typically, researchers compare several MPTs, each equipped with many parameters, especially when the models are implemented in a hierarchical framework. A Bayesian solution is to compute posterior model probabilities and Bayes factors. Both quantities, however, rely on the marginal likelihood, a high-dimensional integral that cannot be evaluated analytically. In this case study, we show how Warp-III bridge sampling can be used to compute the marginal likelihood for hierarchical MPTs. We illustrate the procedure with two published data sets and demonstrate how Warp-III facilitates Bayesian model averaging.

Keywords: multinomial processing tree; Bayesian model comparison; Bayes factor; bridge sampling; Warp-III; posterior model probability; Bayesian model averaging (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s11336-018-9648-3

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