The rise of Markov chain Monte Carlo estimation for psychometric modeling

Research output: Contribution to journalReview article

14 Scopus citations

Abstract

Markov chain Monte Carlo (MCMC) estimation strategies represent a powerful approach to estimation in psychometric models. Popular MCMC samplers and their alignment with Bayesian approaches to modeling are discussed. Key historical and current developments of MCMC are surveyed, emphasizing how MCMC allows the researcher to overcome the limitations of other estimation paradigms, facilitates the estimation of models that might otherwise be intractable, and frees the researcher from certain possible misconceptions about the models.

Original languageEnglish (US)
Article number537139
JournalJournal of Probability and Statistics
DOIs
StatePublished - Dec 1 2009

ASJC Scopus subject areas

  • Statistics and Probability

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