Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)

This book provides a comprehensive framework for constructing and using probabilistic models of complex systems, enabling computers to make decisions based on available information.

With a focus on probabilistic graphical models, the book covers various classes of models, including Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions for dynamical systems and relational data.

The text also discusses the use of the proposed framework for causal reasoning and decision making under uncertainty.

The main technical development is provided in each chapter, along with additional boxes containing techniques, case studies, and empirical cases related to the approach described in the text.

This book is a must-read for anyone interested in modeling and reasoning about complex systems, including computer scientists, statisticians, engineers, and researchers in various fields.