Generating Bayesian Networks from Probability Logic Knowledge Bases
نویسنده
چکیده
We present a method for dynamically generating Bayesian networks from knowledge bases consisting of rst-order probability logic sentences. We present a subset of probability logic su cient for representing the class of Bayesian networks with discrete-valued nodes. We impose constraints on the form of the sentences that guarantee that the knowledge base contains all the probabilistic information necessary to generate a network. We de ne the concept of d-separation for knowledge bases and prove that a knowledge base with independence conditions dened by d-separation is a complete speci cation of a probability distribution. We present a network generation algorithm that, given an inference problem in the form of a query Q and a set of evidence E, generates a network to compute P (QjE). We prove the algorithm to be correct. 2
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Generating Bayesian Networks from Probablity Logic Knowledge Bases
We present a method for dynamically gen erating Bayesian networks from knowledge bases consisting of first-order probability logic sentences. We present a subset of proba bility logic sufficient for representing the class of Bayesian networks with discrete-valued nodes. We impose constraints on the form of the sentences that guarantee that the knowl edge base contains all the probabilistic i...
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We present a method for dynamically generating Bayesian networks from knowledge bases consisting of rst-order probability logic sentences. We present a subset of probability logic suucient for representing the class of Bayesian networks with discrete-valued nodes. We impose constraints on the form of the sentences that guarantee that the knowledge base contains all the probabilistic information...
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