Probabilistic Graphical Models

This book presents a framework for constructing and using probabilistic models of complex systems. The models allow for reasoning and decision-making based on available information. The book focuses on probabilistic models, which provide a more accurate representation of reality by making uncertainty explicit. It discusses a variety of models, including Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions for dynamical systems and relational data. The book is suitable for both basic and advanced techniques, with additional material including case studies and significant concepts drawn from the material in each chapter.