New Algorithms for Graphical Models and Their Applications in Learning

Abstract

Probabilistic graphical models bring together graph theory and probability theory in a powerful formalism for multivariate statistical modelling. Since many machine learning problems involve the modelling of multivariate probability distributions, graphical mod- els can be a good fit to these problems. In this thesis, we show that applying graphical models in machine learning problems can have several advantages: First, it can better capture the nature of the problem. Second, it gives us great flexibility in modelling. Finally, it provides us with