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Synopsis
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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About the Author
Adnan Darwiche is a Professor in the Department of Computer Science at the University of California, Los Angeles.
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Modeling and Reasoning with Bayesian Networks
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Cambridge University Press, 2014
ISBN 10: 1107678420
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Modeling and Reasoning with Bayesian Networks
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Modeling and Reasoning with Bayesian Networks
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Modeling and Reasoning With Bayesian Networks
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Modeling and Reasoning with Bayesian Networks
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Modeling and Reasoning with Bayesian Networks
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Modeling and Reasoning with Bayesian Networks
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Modeling and Reasoning With Bayesian Networks (Paperback)
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ISBN 10: 1107678420
ISBN 13: 9781107678422
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Paperback. Condition: new. Paperback. This 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781107678422
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Modeling and Reasoning with Bayesian Networks
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ISBN 13: 9781107678422
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Modeling and Reasoning with Bayesian Networks
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ISBN 10: 1107678420
ISBN 13: 9781107678422
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