Bayesian Analysis of Mixtures of Factor Analyzers
- 1 May 2001
- journal article
- research article
- Published by MIT Press in Neural Computation
- Vol. 13 (5) , 993-1002
- https://doi.org/10.1162/08997660151134299
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.Keywords
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