Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference (Morgan Kaufmann Series in Representation and Reasoning)

The book "Probabilistic Reasoning in Intelligent Systems" is a comprehensive guide to understanding and utilizing probability theory for reasoning under uncertainty.

It provides a unified perspective on various AI approaches to uncertainty, including Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic.

The book offers practical techniques based on belief networks that enable the combination of theoretical coherence with modern demands of reasoning systems technology.

These techniques include network-propagation methods for modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation.

The application areas covered in the book are diverse, including diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognition, and more.

This book is a must-read for scholars, researchers, professionals, and students in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and management sciences.

It can also serve as an excellent textbook for graduate-level courses in AI, operations research, or applied probability.