Bayesian Artificial Intelligence, Second Edition

This book provides a comprehensive introduction to Bayesian artificial intelligence, covering both theoretical foundations and practical applications. It begins with the basics of probabilistic reasoning and Bayesian networks, then progresses to advanced topics such as learning Bayesian networks from data, causal discovery, and decision making under uncertainty.

The second edition includes new material on Bayesian network inference algorithms, including approximate methods, and expanded coverage of real-world applications. The authors emphasize a hands-on approach, with numerous examples and case studies that illustrate how Bayesian methods can be applied to solve complex AI problems.

Key topics include: Bayesian reasoning and probability calculus, Bayesian networks and their construction, inference in Bayesian networks (exact and approximate), learning parameters and structures, causal discovery, decision networks and influence diagrams, and applications in fields such as medical diagnosis, fault diagnosis, and risk analysis.

The book is suitable for advanced undergraduate and graduate students in computer science, statistics, and engineering, as well as researchers and practitioners interested in probabilistic AI. It assumes some background in probability and programming, but provides self-contained explanations of key concepts.

With its clear exposition and practical focus, this edition remains a definitive resource on Bayesian AI, bridging theory and implementation.