Learning Bayesian Networks
For courses in Bayesian Networks or Advanced Networking focusing on Bayesian networks found in departments of Computer Science, Computer Engineering and Electrical Engineering. Also appropriate as a supplementary text in courses on Expert Systems, Machine Learning, and Artificial Intelligence where the topic of Bayesian Networks is covered. This book provides an accessible and unified discussion of Bayesian networks. It includes discussions of topics related to the areas of artificial intelligence, expert systems and decision analysis, the fields in which Bayesian networks are frequently applied. The author discusses both methods for doing inference in Bayesian networks and influence diagrams. The book also covers the Bayesian method for learning the values of discrete and continuous parameters. Both the Bayesian and constraint-based methods for learning structure are discussed in detail.
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Introduction to Bayesian Networks
More DAGProbability Relationships
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admits a faithful Algorithm 10.5 approximation assume assumption augmented Bayesian network Bayesian network sample binomial called causal DAG causal influence compute concerning the relative conditional distributions conditional independencies conditional probability conditionally independent d-separations DAG G DAG in Figure DAG pattern gp data set decision tree Definition density function determine developed Dirichlet Dirichlet density Dirichlet distribution edge embedded Bayesian network embedded faithfully entails Equality equivalent sample Example exercise to show faithful DAG representation finasteride Gaussian Bayesian network head-to-head meeting hidden common cause hidden node DAG hidden variable hidden variable DAG IND is embedded inducing chain inference influence diagram instantiated joint probability distribution Lemma lung cancer Markov condition means method multivariate normal multivariate normal distribution NASDIP network in Figure node DAG pattern normal distribution obtained outcome parameter parents prior probability probabilistic probability interval random variables satisfies the Markov set of d-separations space subset Suppose updated variable DAG model variance