The purpose of this chapter is to provide an introduction to Bayesian approach within a general framework and develop a Bayesian procedure for analyzing multivariate longitudinal data within the hidden Markov factor analysis framework.
Part of the book: New Insights into Bayesian Inference
The purpose of this chapter is to provide an introduction to the model-based clustering within the Bayesian framework and apply it to asses the heterogeneity of fractional data via finite mixture two-part regression model. The problems related to the number of clusters and the configuration of observations are addressed via Markov Chains Monte Carlo (MCMC) sampling method. Gibbs sampler is implemented to draw observations from the related full conditionals. As a concrete example, the cocaine use data are analyzed to illustrate the merits of the proposed methodology.
Part of the book: Data Clustering