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 maxi...

We propose simple transformation of the hidden states in variational Bayesian factor analysis models to speed up the learning procedure. The speed-up is achieved by using proper parameterization of the posterior approximation which allows joint optimization of its individual factors, thus the transformation is theoretically justified. We derive the transformation formulae for variational Bayesi...

Factor analysis is a standard method for multivariate analysis. The sampling model in the most popular factor analysis is Gaussian and has thus often been criticized for its lack of robustness. A simple robust extension of the Gaussian factor analysis model is obtained by replacing the multivariate Gaussian distribution with a multivariate t-distribution. We develop computational methods for bo...

the  main  assumptions  in  liner  mixed  model  are normality  and  independency  of  random  effect component.   unfortunately,   these   two  assumptions   might   be  unrealistic   in  some   situations. therefore, in this paper, we will discuss about the analysis of bayesian analysis of non-normal and non-independent mixed model using skew-normal/independent distributions, and finally, thi...

uncertainty associated with mixing models is often substantial, but has not yet been fully incorporated in models. the objective of this study is to develop and apply a bayesian-mixing model that estimates probability distributions of source contributions to a mixture associated with multiple sources for assessing the uncertainty estimation in sediment fingerprinting in zidasht catchment, iran....