title: Bayesian analysis of mixtures of factor analyzers.
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.
authors.affiliation: National Institute of Bioscience and Human-Technology, Tsukuba 305-8566, Japan.
authors.forename: A
authors.initials: A
authors.lastname: Utsugi
authors.forename: T
authors.initials: T
authors.lastname: Kumagai
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D005163:Factor Analysis, Statistical; D016013:Likelihood Functions