| Summary: | Context specific independence can provide compact representation of the conditional
probabilities in Bayesian networks when some variables are only relevant in
specific contexts. We present eve-tree, an algorithm that exploits context specific
independence in clique tree propagation. This algorithm is based on a query-based
contextual variable elimination algorithm (eve) that eliminates in turn the variables
not needed in an answer. We extend eve to producing the posterior probabilities
of all variables efficiently and allow the incremental addition of evidence. We perform
experiments that compare eve-tree and Hugin using parameterized random
networks that exhibit various amounts of context specific independence, as well as a
standard network, the Insurance network. Our empirical results show that eve-tree
is efficient, both in time and in space, as compared to the Hugin architecture, on
computing posterior probabilities for Bayesian networks that exhibit context specific
independence. === Science, Faculty of === Computer Science, Department of === Graduate
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