Determining Missing Constraint Values in Bayesian Networks with
Determining Missing Constraint Values in. Bayesian Networks with Maximum Entropy: A. First Step Towards a Generalized Bayesian. Network. Dawn E. Holmes.
Determining Missing Constraint Values in Bayesian Networks with Maximum Entropy: A First Step Towards a Generalized Bayesian Network Dawn E. Holmes Department of Statistics and Applied Probability, South Hall, University of California, Santa Barbara, CA 93106, USA. Abstract. The author’s past work in this area has shown that the probability of a state of a Bayesian network, found using the standard Bayesian techniques, could be equated to the Maximum Entropy solution and that this result enabled us to find minimally prejudiced estimates of missing information in Bayesian networks. In this paper we show that in the class of Bayesian networks known as Bayesian trees, we are able to determine missing constraint values optimally without the use of Bayesian techniques, using only the Maximum Entropy Formalism. We also show that it is possible to produce a generalized Bayesian network, which is specified entirely within the Maximum Entropy formalism. Keywords: Bayesian networks, Maximum entropy, d-separation. PACS: 02.50.Cw, 89.70.+c , 05.70.–a, 65.40.Gr
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Received April 23, 2013; revised May 23, 2013; accepted May 30, 2013 ... This is an open access article distributed under the Creative Commons Attribution Li-.
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