3. Bayesian Methods for Estimating Structural Equation Models

  1. Xin-Yuan Song and
  2. Sik-Yum Lee

Published Online: 18 JUL 2012

DOI: 10.1002/9781118358887.ch3

Basic and Advanced Bayesian Structural Equation Modeling: With Applications in the Medical and Behavioral Sciences

Basic and Advanced Bayesian Structural Equation Modeling: With Applications in the Medical and Behavioral Sciences

How to Cite

Song, X.-Y. and Lee, S.-Y. (2012) Bayesian Methods for Estimating Structural Equation Models, in Basic and Advanced Bayesian Structural Equation Modeling: With Applications in the Medical and Behavioral Sciences, John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9781118358887.ch3

Author Information

  1. Department of Statistics, The Chinese University of Hong Kong

Publication History

  1. Published Online: 18 JUL 2012
  2. Published Print: 24 AUG 2012

Book Series:

  1. Wiley Series in Probability and Statistics

Book Series Editors:

  1. Walter A. Shewhart and
  2. Samuel S. Wilks

ISBN Information

Print ISBN: 9780470669525

Online ISBN: 9781118358887

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Keywords:

  • Bayesian estimation;
  • Markov chain Monte Carlo (MCMC);
  • posterior distribution;
  • prior distribution;
  • statistical inference;
  • structural equation models (SEMs);
  • WinBUGS

Summary

This chapter introduces an attractive Bayesian approach which can be effectively applied to analyze not only the standard structural equation models (SEMs) but also useful generalizations of SEMs that have been developed in recent years. It provides an introduction to the Bayesian approach for conducting statistical inferences of SEMs. The chapter also presents the basic ideas of the Bayesian approach to estimation, including the prior distribution. Further, it considers Posterior analyses through applications of some Markov chain Monte Carlo (MCMC) methods and presents an application of the MCMC methods. Finally, the chapter describes how to use the WinBUGS software to obtain Bayesian estimation and to conduct simulation studies.

Controlled Vocabulary Terms

Bayes estimator; inferential statistics; Markov chain Monte Carlo estimation; noninformative prior distribution; posterior predictive distribution; WinBUGS