Bayesian Artificial Intelligence by Kevin B Korb
The second edition of this bestseller provides a practical and accessible introduction to the main concepts, foundation, and applications of Bayesian networks. This edition contains a new chapter on Bayesian network classifiers and a new section on object-oriented Bayesian networks, along with new applications and case studies. It includes a new-
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Music Data Analysis
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Visualization and Verbalization of Data
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Introduction to Data Technologies
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Combinatorial Inference in Geometric Data Analysis
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Time Series Clustering and Classification
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Correspondence Analysis and Data Coding with Java and R
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Semisupervised Learning for Computational Linguistics
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Pattern Recognition Algorithms for Data Mining
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Design and Modeling for Computer Experiments
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Microarray Image Analysis
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Data Science Foundations
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Textual Data Science with R
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Modeling Data With Functional Programming in R
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Statistics in MATLAB
… useful insights on Bayesian reasoning… There are extensive examples of applications and case studies. … The exposition is clear, with many comments that help set the context for the material that is covered. The reader gets a strong sense that Bayesian networks are a work in progress.
—John H. Maindonald, International Statistical Review (2011), 79
Praise for the First Edition:
… this excellent book would also serve well for final year undergraduate courses in mathematics or statistics and is a solid first reference text for researchers wanting to implement Bayesian belief network (BBN) solutions for practical problems. … beautifully presented, nicely written, and made accessible. Mathematical ideas, some quite deep, are presented within the flow but do not get in the way. This has the advantage that students can see and interpret the mathematics in the practical context, whereas practitioners can acquire, to personal taste, the mathematical seasoning. If you are interested in applying BBN methods to real-life problems, this book is a good place to start…
—Journal of the Royal Statistical Society, Series A, Vol. 157(3)
Kevin B. Korb is a Reader in the Clayton School of Information Technology at Monash University in Australia. He earned his Ph.D. from Indiana University. His research encompasses causal discovery, probabilistic causality, evaluation theory, informal logic and argumentation, artificial evolution, and philosophy of artificial intelligence.
Ann E. Nicholson an Associate Professor in the Clayton School of Information Technology at Monash University in Australia. She earned her Ph.D. from the University of Oxford. Her research interests include artificial intelligence, probabilistic reasoning, Bayesian networks, knowledge engineering, plan recognition, user modeling, evolutionary ethics, and data mining
| SKU | Unavailable |
| ISBN 13 | 9781032477657 |
| ISBN 10 | 1032477652 |
| Title | Bayesian Artificial Intelligence |
| Author | Kevin B Korb |
| Series | Chapman And Hall Crc Computer Science And Data Analysis |
| Condition | Unavailable |
| Binding Type | Paperback |
| Publisher | CRC Press |
| Year published | 2023-01-21 |
| Number of pages | 492 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |