In a society which produces and consumes an ever increasing amount of information, methods which can make sense out of all this data become of crucial importance.
Machine learning tries to develop models which can make the information load accessible.
This is especially relevant for high-dimensional data which cannot be visualized anymore. This thesis investigates these three questions by trying to construct complex models from simple ones. The decomposition into simpler parts can also be found in the methods used for estimating the parameter values of these models. The algorithms for the simple models constitute the core of the algorithms for the complex ones. A promising alternative to GMMs are the recently proposed mixtures of latent variable models.