Bayesian analysis of multivariate longitudinal data using latent structures with applications to medical data KU Leuven
In biomedical science, sociology, psychology, etc., it is often of interest to understand how multiple variables are associated. In many cases, the observed outcomes are not of direct interest, instead they are considered as manifestations of one or more underlying latent characteristics. When measured repeatedly over time, interest is then often placed on the evolution of the latent characteristics (variables) and/or the effects of ...