Bayesiansk statistik
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Bayesian Artificial Intelligence
Udgivet af Taylor & Francis Ltd
ISBN
9781032477657
Om denne bog
Updated and expanded, Bayesian Artificial Intelligence, Second Edition provides a practical and accessible introduction to the main concepts, foundation, and applications of Bayesian networks. It focuses on both the causal discovery of networks and Bayesian inference procedures. Adopting a causal interpretation of Baye…
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Bogdetaljer
Forfatter
Kevin B. Korb, Ann E. Nicholson
Forlag
Taylor & Francis Ltd
Kategori
Bayesiansk statistik
ISBN-13
9781032477657
EAN
9781032477657
Format
Hæftet
Sprog
Engelsk
sider
492
Produktbeskrivelse
Updated and expanded, Bayesian Artificial Intelligence, Second Edition provides a practical and accessible introduction to the main concepts, foundation, and applications of Bayesian networks. It focuses on both the causal discovery of networks and Bayesian inference procedures. Adopting a causal interpretation of Bayesian networks, the authors discuss the use of Bayesian networks for causal modeling. They also draw on their own applied research to illustrate various applications of the technology.New to the Second EditionNew chapter on Bayesian network classifiersNew section on object-oriented Bayesian networksNew section that addresses foundational problems with causal discovery and Markov blanket discoveryNew section that covers methods of evaluating causal discovery programsDiscussions of many common modeling errorsNew applications and case studiesMore coverage on the uses of causal interventions to understand and reason with causal Bayesian networksIllustrated with real case studies, the second edition of this bestseller continues to cover the groundwork of Bayesian networks. It presents the elements of Bayesian network technology, automated causal discovery, and learning probabilities from data and shows how to employ these technologies to develop probabilistic expert systems.Web ResourceThe book’s website at www.csse.monash.edu.au/bai/book/book.html offers a variety of supplemental materials, including example Bayesian networks and data sets. Instructors can email the authors for sample solutions to many of the problems in the text.
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