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.
The 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.
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)
PROBABILISTIC REASONING Bayesian Reasoning Reasoning under uncertainty Uncertainty in AI Probability calculus Interpretations of probability Bayesian philosophy The goal of Bayesian AI Achieving Bayesian AI Are Bayesian networks Bayesian? Introducing Bayesian Networks Introduction Bayesian network basics Reasoning with Bayesian networks Understanding Bayesian networks More examples Inference in Bayesian Networks Introduction Exact inference in chains Exact inference in polytrees Inference with uncertain evidence Exact inference in multiply-connected networks Approximate inference with stochastic simulation Other computations Causal inference Decision Networks Introduction Utilities Decision network basics Sequential decision making Dynamic Bayesian networks Dynamic decision networks Object-oriented Bayesian networks Applications of Bayesian Networks Introduction A brief survey of BN applications Cardiovascular risk assessment Goulburn Catchment Ecological Risk Assessment Bayesian poker Ambulation monitoring and fall detection A Nice Argument Generator (NAG) LEARNING CAUSAL MODELS Learning Probabilities Introduction Parameterizing discrete models Incomplete data Learning local structure Bayesian Network Classifiers Introduction Naive Bayes models Semi-naive Bayes models Ensemble Bayes prediction The evaluation of classifiers Learning Linear Causal Models Introduction Path models Constraint-based learners Learning Discrete Causal Structure Introduction Cooper and Herskovits' K2 MDL causal discovery Metric pattern discovery CaMML: Causal discovery via MML CaMML stochastic search Problems with causal discovery Evaluating causal discovery KNOWLEDGE ENGINEERING Knowledge Engineering with Bayesian Networks Introduction The KEBN process Stage 1: BN structure Stage 2: probability parameters Stage 3: decision structure Stage 4: utilities (preferences) Modeling example: missing car Incremental modeling Adaptation KEBN Case Studies Introduction Bayesian poker revisited An intelligent tutoring system for decimal understanding Goulburn Catchment Ecological Risk Assessment Cardiovascular risk assessment Appendix A: Notation Appendix B: Software Packages References Index A Summary, Notes, and Problems appear at the end of each chapter.
Series: Chapman & Hall/CRC Computer Science & Data Analysis
Tertiary; University or College
Number Of Pages: 491
Published: 7th December 2010
Publisher: Taylor & Francis Inc
Country of Publication: US
Dimensions (cm): 23.5 x 15.9
Weight (kg): 0.82
Edition Number: 2
Edition Type: New edition