Generating Bayesian Networks from Probability Logic Knowledge Bases (1994)
| Venue: | In Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence |
| Citations: | 49 - 8 self |
BibTeX
@INPROCEEDINGS{Haddawy94generatingbayesian,
author = {Peter Haddawy},
title = {Generating Bayesian Networks from Probability Logic Knowledge Bases},
booktitle = {In Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence},
year = {1994},
pages = {262--269},
publisher = {Morgan Kaufmann}
}
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Abstract
We present a method for dynamically generating Bayesian networks from knowledge bases consisting of first-order probability logic sentences. We present a subset of probability 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 knowledge base contains all the probabilistic information necessary to generate a network. We define the concept of d-separation for knowledge bases and prove that a knowledge base with independence conditions defined by d-separation is a complete specification 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. 1 Introduction The flexibility of Bayesian networks for representing probabilistic dependencies and the relative efficiency of computational techniques for p...
