Probabilistic Graphical Models are a thriving research area at the interface of probability theory and graph theory. Thanks to their modularity and expressive power, they are becoming a unifying language for the formulation of complex models. On the one hand, such models raise deep questions in statistics and optimization. At the same time, they allow to address challenging applications of image analysis in computer vision, the life sciences, earth sciences and industry.