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Title: Irrelevance and parameter learning in Bayesian networks
Authors: Zhang, Nevin Lianwen
Keywords: Bayesian networks
Parameter learning
Issue Date: 1996
Citation: Artificial Intelligence, v. 88, 1996, p. 359-373
Abstract: It is possible to learn the parameters of a given Bayesian network structure from data because those parameters influence the probability of observing the data. However, some of the parameters are irrelevant to the probability of observing a particular data case. This paper shows how such irrelevancies can be exploited to speedup various algorithms for parameter learning in Bayesian networks.
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