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 using only the maximum entropy formalism. Bayesian networks that are specified entirely within the maximum entropy formalism, whether or not information is missing, are called generalized Bayesian networks. It is expected that further work will fully generalize this result.
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References
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Holmes, D.E. (2008). Toward a Generalized Bayesian Network. In: Holmes, D.E., Jain, L.C. (eds) Innovations in Bayesian Networks. Studies in Computational Intelligence, vol 156. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85066-3_11
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DOI: https://doi.org/10.1007/978-3-540-85066-3_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85065-6
Online ISBN: 978-3-540-85066-3
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