Modeling and Reasoning with Bayesian Networks
Modeling and Reasoning with Bayesian Networks by Adnan Darwiche, published by Cambridge University Press on August 7, 2014, is a comprehensive resource that introduces the formal foundations and practical applications of Bayesian networks. Spanning 562 pages, this book offers an extensive discussion on techniques for constructing Bayesian networks that model real-world situations, including methods for synthesizing models, learning from data, and debugging through sensitivity analysis.
Readers will find a thorough exploration of both exact and approximate inference algorithms, presented at theoretical and practical levels. The author caters to a wide audience by assuming minimal prior knowledge, providing in-depth discussions for those with a theoretical inclination while also offering practical details that serve as an algorithmic guide for system developers. Topics such as artificial intelligence, probability, and discrete mathematics are woven throughout, making this edition a valuable addition for those interested in the intersection of these fields.
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This book provides a thorough introduction to the formal foundations and practical applications of Bayesian networks. It provides 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. It also treats exact and approximate inference algorithms at both theoretical and practical levels. 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 the system developer.
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