Open Access
Translator Disclaimer
June 2016 Incorporating Marginal Prior Information in Latent Class Models
Tracy A. Schifeling, Jerome P. Reiter
Bayesian Anal. 11(2): 499-518 (June 2016). DOI: 10.1214/15-BA959


We present an approach to incorporating informative prior beliefs about marginal probabilities into Bayesian latent class models for categorical data. The basic idea is to append synthetic observations to the original data such that (i) the empirical distributions of the desired margins match those of the prior beliefs, and (ii) the values of the remaining variables are left missing. The degree of prior uncertainty is controlled by the number of augmented records. Posterior inferences can be obtained via typical MCMC algorithms for latent class models, tailored to deal efficiently with the missing values in the concatenated data. We illustrate the approach using a variety of simulations based on data from the American Community Survey, including an example of how augmented records can be used to fit latent class models to data from stratified samples.


Download Citation

Tracy A. Schifeling. Jerome P. Reiter. "Incorporating Marginal Prior Information in Latent Class Models." Bayesian Anal. 11 (2) 499 - 518, June 2016.


Published: June 2016
First available in Project Euclid: 18 June 2015

zbMATH: 1357.62130
MathSciNet: MR3472000
Digital Object Identifier: 10.1214/15-BA959

Keywords: categorical , Dirichlet process , missing , mixture , stratified , survey

Rights: Copyright © 2016 International Society for Bayesian Analysis


Vol.11 • No. 2 • June 2016
Back to Top