Bayesian Analysis of Mixtures of Factor Analyzers
Akio Utsugi and Toru Kumagai
Neural Computation, vol. 13, no. 5, pp. 993-1002.
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.
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.