Abstract
Bayesian networks, also known as Bayesian belief networks, are a connectionist knowledge representation that is popular in AI for reasoning under uncertainty. A Bayesian network is a directed acyclic graph augmented with conditional probability distributions residing in each node. An important problem on Bayesian networks is that of finding the most probable network assignment, or explanation, that is consistent with a given set of observances called the evidence. In an earlier paper (1998), the author presented an algorithm that allows the explanation problem on Bayesian networks to be modeled by integer linear programming. In this paper, he presents the results of applying this algorithm to a group of Bayesian networks.
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