Recent Advances in Probabilistic Graphical Models

Probabilistic graphical models constitute a fundamental tool for the development of intelligent systems. They provide a sound and well-founded approach for performing inference and belief updating in complex domains endowed with uncertainty. A probabilistic graphical model is the result of the combination of a qualitative component (a graph) encoding conditional independence relationships among the variables in the system and a quantitative component consisting of a collection of local probability distributions matching the independence properties specified by the graph. The union of the two components provides a compact representation of the joint probability distribution over the domain being modeled. Bayesian networks are the most prominent type of probabilistic graphical models and have experienced a remarkable methodological development during the past two decades. This has come along with a wide variety of successful applications in different domains. Regardless of the increasing interest in the area, probabilistic graphical models are still facing a number of challenges, covering modeling, inference and learning. This special issue contains seven papers that contribute to the methodological development beyond the current state-of-the-art knowledge. Five of the papers were selected among the contributions presented at the 15th Conference of the Spanish Association of Artificial Intelligence (CAEPIA’2013, Madrid, Spain, September 17–20, 2013). These papers were substantially extended and went through a new and strict review process. The other two papers were invited contributions not presented at the conference and went through the same review process. The first three papers deal with hybrid models, where both discrete and continuous variables coexist. Lucas and Hommersom propose a framework for handling causal independence covering discrete and continuous variables simultaneously. The methodology is based on the convolution concept from probability theory, and the