without making it a mathematical exercise in futility or by dumbing it down too much to make it a & guide'. This book is an interesting read and knowing the KDD genre, it's few and far between when one can say these words about a machine learning book
Bayesian Artificial Intelligence (Chapman & Hall Crc Computer Science and Data Analysis). Kevin B. Korb Ann E. Nicholson.
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As the power of Bayesian techniques has become more fully realized, the field of artificial intelligence has embraced Bayesian methodology and integrated it to the point where an introduction to Bayesian techniques is now a core course in many computer science programs. Unlike other books on the subject, Bayesian Artificial Intelligence keeps mathematical detail to a minimum and covers a broad range of topics.
Bayesian biostatistics and diagnostic medicine By Lyle D. Broemeling.
Items related to Bayesian Artificial Intelligence (Chapman & Hall/CRC. Ann E. Nicholson an Associate Professor in the Clayton School of Information Technology at Monash University in Australia. Korb; Ann E. Nicholson Bayesian Artificial Intelligence (Chapman & Hall/CRC Computer Science & Data Analysis). ISBN 13: 9781439815915. from the University of Oxford.
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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.
graphics, data visualization, imaging, Bayesian data analysis, computer security, and internet data analysis.
The scope of the series includes computational statistics, data mining, machine learning, exploratory data analysis, pattern recognition, AI, statistical and computational learning theory, statistical software and graphics, data visualization, imaging, Bayesian data analysis, computer security, and internet data analysis. The inclusion of real-life examples and applications is highly encouraged, as is specific software implementation
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 Edition
New chapter on Bayesian network classifiers New section on object-oriented Bayesian networks New section that addresses foundational problems with causal discovery and Markov blanket discovery New section that covers methods of evaluating causal discovery programs Discussions of many common modeling errors New applications and case studies More coverage on the uses of causal interventions to understand and reason with causal Bayesian networks
Illustrated 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.
Author: Ann E. Nicholson,Kevin B. Korb
Category: Computers and Technology
Publisher: CRC Press; 2 edition (December 16, 2010)
Pages: 491 pages
ePUB size: 1120 kb
FB2 size: 1996 kb
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