Modeling and Reasoning with Bayesian Networks eBook: Adnan Darwiche: Amazon.fr: Amazon Media EU S.à r.l.

  • 562 pages
  • Anglais
  • Format Kindle
Modeling and Reasoning with Bayesian Networks eBook: Adnan Darwiche: Amazon.fr: Amazon Media EU  S.à r.l.

ಭ Free Read Format Kindle ᘫ Modeling and Reasoning with Bayesian Networks eBook: Adnan Darwiche: Amazon.fr: Amazon Media EU S.à r.l. ಿ Ebook Author Adnan Darwiche ೫ 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 Computing ReviewsThis book is 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 treatment of 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 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 the system developer. 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