Modeling and reasoning with Bayesian networks.

*(English)*Zbl 1231.68003
Cambridge: Cambridge University Press (ISBN 978-0-521-88438-9/hbk). xii, 548 p. (2009).

Publisher’s description: A thorough introduction to the formal foundations and practical applications of Bayesian networks is given, and an extensive discussion of techniques for building Bayesian networks that model real-world situations, including techniques for synthesizing models from design, learning models from data, and debugging models using sensitivity analysis, is provided. Exact and approximate inference algorithms at both theoretical and practical levels are treated. The treatment of the exact algorithms covers the main inference paradigms based on elimination and conditioning and includes advanced methods for compiling Bayesian networks, time-space tradeoffs and exploiting local structure of massively connected networks. The treatment of the approximate algorithms covers the main inference paradigms based on sampling and optimization and includes influential algorithms such as importance sampling, MCMC and belief propagation. The author assumes very little background on the covered subjects, supplying in-depth discussions for theoretically inclined readers and enough practical details to provide an algorithmic cookbook for system developers.

##### MSC:

68-01 | Introductory exposition (textbooks, tutorial papers, etc.) pertaining to computer science |

68T01 | General topics in artificial intelligence |

68T05 | Learning and adaptive systems in artificial intelligence |

68T37 | Reasoning under uncertainty in the context of artificial intelligence |