1. Introduction; 2. Propositional logic; 3. Probability calculus; 4. Bayesian networks; 5. Building Bayesian networks; 6. Inference by variable elimination; 7. Inference by factor elimination; 8. Inference by conditioning; 9. Models for graph decomposition; 10. Most likely instantiations; 11. The complexity of probabilistic inference; 12. Compiling Bayesian networks; 13. Inference with local structure; 14. Approximate inference by belief propagation; 15. Approximate inference by stochastic sampling; 16. Sensitivity analysis; 17. Learning: the maximum likelihood approach; 18. Learning: the Bayesian approach; Appendix A: notation; Appendix B: concepts from information theory; Appendix C: fixed point iterative methods; Appendix D: constrained optimization.
This book introduces the formal foundations and practical applications of Bayesian networks.
Adnan Darwiche is a Professor in the Department of Computer Science at UCLA.
'… both practical and advanced … The first five chapters are
sufficient for students and practitioners to gain the necessary
knowledge in order to build Bayesian networks for moderately sized
applications with the aid of a software tool … All major inference
methods are covered in later chapters which allow researchers and
software developers to implement their own software systems
tailored to their needs … It is a comprehensive book that can be
used for self study by students and newcomers to the field or as a
companion for courses on probabilistic reasoning. Experienced
researchers may also find deeper information on some topics. In my
opinion, the book should definitely be [on] the bookshelf of
everyone who teaches Bayesian networks and builds probabilistic
reasoning agents.' Artificial Intelligence
'[This] book will make an excellent textbook; it covers topics
suitable for both undergraduate and graduate courses. It will also
help practitioners get a firm grasp of the fundamentals of modeling
and inference with BNs, as well as some recent advances.' ACM
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