Abstract
Given a Bayesian network relative to a set I of discrete random variables, we are interested in computing the probability distribution P A or the conditional probability distribution P A|B , where A and B are two disjoint subsets of I. The general idea of the algorithm of successive restrictions is to manage the succession of summations on all random variables out of the target A in order to keep on it a structure less constraining than the Bayesian network, but which allows saving in memory ; that is the structure of Bayesian Network of Level Two.
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Smail, L., Raoult, J.P. (2005). Successive Restrictions Algorithm in Bayesian Networks. In: Famili, A.F., Kok, J.N., Peña, J.M., Siebes, A., Feelders, A. (eds) Advances in Intelligent Data Analysis VI. IDA 2005. Lecture Notes in Computer Science, vol 3646. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552253_37
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DOI: https://doi.org/10.1007/11552253_37
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