Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms (Synthesis Lectures on Artificial Intelligence and Machine Learning, 23)

I have always appreciated Rina Dechter's work, particularly for the focus on viewing multiple inference problems and methods, developed over the years in different AI communities, in a unified framework. Such unified understanding provides a much more articulated way of thinking about inference problems, because the knowledge about one method can be translated and borrowed for another much more easily.This book is a concise, yet deep, overview of many methods for many different problems seen from a graphical models point of view (deterministic constraint processing, probabilistic inference in Bayesian and Markov networks, cost networks, mixed networks, MAP, MPE). It starts by presenting a unified view of them all, and proceeds by discussing multiple methods, from simpler to more sophisticated, in a gradual and natural sequence that is easy to understand. It covers inference methods such as belief propagation, variable elimination, junction tree, and search methods (OR search and then AND-OR search). At the end it discusses issues in integrating these methods, and how they relate to each other. After reading the book I had much better idea of where all these elements fit within this landscape.