Kevin B. Korb – författare

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 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 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.

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 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.

This book constitutes the refereed proceedings of the Second Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD-98, held in Melbourne, Australia, in April 1998. The book presents 30 revised full papers selected from a total of 110 submissions; also included are 20 poster presentations. The papers contribute new results to all current aspects in knowledge discovery and data mining on the research level as well as on the level of systems development. Among the areas covered are machine learning, information systems, the Internet, statistics, knowledge acquisition, data visualization, software reengineering, and knowledge based systems.

This book constitutes the refereed proceedings of the 4th Australian Conference on Artificial Life, ACAL 2009, held in Melbourne, Australia, in December 2009. The 27 revised full papers presented were carefully reviewed and selected from 60 submissions. Research in Alife covers the main areas of biological behaviour as a metaphor for computational models, computational models that reproduce/duplicate a biological behaviour, and computational models to solve biological problems. Thus, Alife features analyses and understanding of life and nature and helps modeling biological systems or solving biological problems. The papers are organized in topical sections on alife art, game theory, evolution, complex systems, biological systems, social modelling, swarm intelligence, and heuristics.