Bayesian Analysis of Mixtures of Factor Analyzers

Akio Utsugi and Toru Kumagai

Neural Computation, vol. 13, no. 5, pp. 993-1002.

Abstract:

For Bayesian inference on the mixture of factor analyzers (MFA), 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 (MAP) estimation algorithm with hyperparameter search. The behaviors of the Gibbs sampler and the deterministic algorithm are compared on a simulation experiment.




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