Extending Recurrence Local Computation Approach Towards Ordering Composite Beliefs in Multiply Connected Bayesian Belief Networks
نویسندگان
چکیده
The Recurrence Local Computation Method (RLCM) for nding the most probable explanations (MPE) in a Bayesian belief network is valuable in assisting human beings to explain the possible causes of a set of evidences. However, RLCM works only on singly connected belief networks. This paper presents an extension of the RLCM which can be applied to multiply connected belief networks for nding arbitrary number of MPEs.
منابع مشابه
A recurrence local computation approach towards ordering composite beliefs in bayesian belief networks
Finding the l Most Probable Explanations (MPE) of a given evidence, S e , in a Bayesian belief network can be formulated as identifying and ordering a set of composite hypotheses, H i s, of which the posterior probabilities are the l largest; i.e., Pr(H 1 jS e) ::: Pr(H l jS e). When an order includes all the composite hypotheses in the network in order to nd all the probable explanations, it b...
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