Fitting the independent factor analysis model using the MCMC algorithm

Independent factor analysis is a recent and novel latent variable model, in which the factors are supposed to be mutually independent and not necessarily Gaussian distributed. The factors are modeled by Gaussian mixtures that are quite flexible to approximate any probability density function. The model estimation can be quite promisingly solved by the EM algorithm when the number of factors is not too high. However, the computational burden needed to fit the model grows rapidly with the number of factors and the number of terms in the mixture involved. In any but the simplest cases, other estimation procedures have to be employed. In this work, an MCMC approach, based on the Gibbs sampler algorithm, is proposed. Its estimation performances are compared with the ordinary EM algorithm on real and simulated data.