Bayesian Artificial Intelligence, Second Edition Apr 2026

Reviewers from the International Statistical Review highlight it as a vital resource for creating human-made artifacts (AI) capable of reasoning from incomplete evidence. It is widely used by researchers in statistics, engineering, and AI to address complex problems without the "overfitting" risks common in traditional machine learning.

: Details the mechanics of building and using networks for causal modeling , focusing on causal discovery and inference procedures. Bayesian Artificial Intelligence, Second Edition

: Provides discussions on common modeling errors and methods for evaluating causal discovery programs. : Provides discussions on common modeling errors and

: Covers the theoretical groundwork and provides insights into probabilistic reasoning, including its importance in fields like the legal system. Korb and Ann E

The book is structured into three primary parts to guide readers through the technology and its implementation:

is a comprehensive textbook by Kevin B. Korb and Ann E. Nicholson that provides a practical introduction to the concepts, foundations, and applications of Bayesian networks . Published as part of the Chapman & Hall/CRC Machine Learning & Pattern Recognition series, it bridges the gap between statistical science and computer science. Core Focus and Structure

This edition expanded on the original text with several notable additions: