Bayesian Artificial IntelligenceUpdated 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 dis |
Contents
LEARNING CAUSAL MODELS | 181 |
KNOWLEDGE ENGINEERING | 293 |
A Notation | 405 |
B Software Packages | 409 |
Bibliography | 417 |
Other editions - View all
Bayesian Artificial Intelligence, Second Edition Kevin B. Korb,Ann E. Nicholson No preview available - 2010 |
Common terms and phrases
action added algorithm allows alternative analysis application approach arcs argument Artificial Intelligence assessment Bayes Bayesian networks belief called cancer causal causal discovery causal model cause chance Chapter classification combination common complex conditional consider decision decision network dependencies described developed direct discrete distribution domain effect elicitation engineering equivalent estimate et al evaluation evidence example expert Figure function given gives graph hand independent inference intervention knowledge learning linear machine Markov means measure methods missing node Note observed parameters parents particular path performance positive possible posterior predictive present prior probabilistic probability problem reasoning relation represent risk sample selection shown shows simple statistics step structure Table tion tree true uncertainty updating utility values variables