Bayesian Analysis of Mixtures of Factor Analyzers

Authors:
Akio Utsugi;Toru Kumagai
Affiliations:
National Institute of Bioscience and Human-Technology, Tsukuba 305-8566, Japan;National Institute of Bioscience and Human-Technology, Tsukuba 305-8566, Japan
Venue:
Neural Computation
Year:
2001

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Abstract

For Bayesian inference on the mixture of factor analyzers, natural conjugate priors on the parameters are introduced, and then a Gibbs sampler that generates parameter samples following the posterior is constructed. In addition, a deterministic estimation algorithm is derived by taking modes instead of samples from the conditional posteriors used in the Gibbs sampler. This is regarded as a maximum a posteriori estimation algorithm with hyperparameter search. The behaviors of the Gibbs sampler and the deterministic algorithm are compared on a simulation experiment.