Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Textbook offers an accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. For graduate-level courses in AI, operations research, and applied probability. Annotation copyright Book News, Inc. Portland, Or.
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algorithm arrows assess assigned assume assumptions axioms Bayes Bayesian network BEL(x belief distribution belief functions belief revision belief updating birds burglary calculate causal Chapter cliques combination computed conclusion conditional independence conditional probabilities consequences consider consistent constraints criterion d-separation decision decision tree default logic default rules dependencies encoded estimate evidential example explanation extension Figure given hypothesis impact inference influence diagram instantiated interpretation knowledge base likelihood logic lottery Markov blanket Markov network matrix measures messages method neighbors node normally observed obtained optimal parameters parents Penguin plausible polytree possible posterior probability prior probability probabilistic model probability distribution probability theory problem processor propagation properties proposition qualitative query reasoning relationships relevant represent representation requires scheme Section semantics sentences singly connected specified statements structure subset switch Theorem topology triplets TRUE Tweety uncertainty undirected graphs variables vector weight yields