In recent years considerable progress has been made in the area of probabilistic graphical models, in particular Bayesian networks and influence diagrams. Probabilistic graphical models have become mainstream in the area of uncertainty in artificial intelligence;contributions to the area are coming from computer science, mathematics, statistics and engineering.

Foundations.- Markov Equivalence in Bayesian Networks.- A Causal Algebra for Dynamic Flow Networks.- Graphical and Algebraic Representatives of Conditional Independence Models.- Bayesian Network Models with Discrete and Continuous Variables.- Sensitivity Analysis of Probabilistic Networks.- Inference.- A Review on Distinct Methods and Approaches to Perform Triangulation for Bayesian Networks.- Decisiveness in Loopy Propagation.- Lazy Inference in Multiply Sectioned Bayesian Networks Using Linked Junction Forests.- Learning.- A Study on the Evolution of Bayesian Network Graph Structures.- Learning Bayesian Networks with an Approximated MDL Score.- Learning of Latent Class Models by Splitting and Merging Components.- Decision Processes.- An Efficient Exhaustive Anytime Sampling Algorithm for Influence Diagrams.- Multi-currency Influence Diagrams.- Parallel Markov Decision Processes.- Applications.- Applications of HUGIN to Diagnosis and Control of Autonomous Vehicles.- Biomedical Applications of Bayesian Networks.- Learning and Validating Bayesian Network Models of Gene Networks.- The Role of Background Knowledge in Bayesian Classification.