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Bayesian model selection for mixtures of structural equation models with an unknown number of components
Author(s) -
Lee SikYum,
Song XinYuan
Publication year - 2003
Publication title -
british journal of mathematical and statistical psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.157
H-Index - 51
eISSN - 2044-8317
pISSN - 0007-1102
DOI - 10.1348/000711003321645403
Subject(s) - bayes factor , gibbs sampling , bayesian probability , bayes' theorem , structural equation modeling , model selection , path (computing) , mathematics , posterior probability , computer science , bayesian hierarchical modeling , prior probability , selection (genetic algorithm) , latent variable , bayesian experimental design , bayesian inference , bayesian statistics , statistics , artificial intelligence , programming language
This paper considers mixtures of structural equation models with an unknown number of components. A Bayesian model selection approach is developed based on the Bayes factor. A procedure for computing the Bayes factor is developed via path sampling, which has a number of nice features. The key idea is to construct a continuous path linking the competing models; then the Bayes factor can be estimated efficiently via grids in [0, 1] and simulated observations that are generated by the Gibbs sampler from the posterior distribution. Bayesian estimates of the structural parameters, latent variables, as well as other statistics can be produced as by‐products. The properties and merits of the proposed procedure are discussed and illustrated by means of a simulation study and a real example.