Inference in Bayesian Networks :

Three kinds of independence are of interest in the context of Bayesian networks, namely conditional independence, independence of causal in uence, and context-speci c independence. It is well-known that conditional independence enables one to factorize a joint probability into a list of conditional probabilities and thereby renders inference feasible. It has recently been shown that independence of causal in uence leads to further factorizations of some of the conditional probabilities and consequently makes inference faster. This paper studies context-speci c independence. We show that context-speci c independence can be used to further decompose some of the conditional probabilities. We present an inference algorithm that takes advantage of the decompositions and provide, for the rst time, empirical evidence that demonstrates the computational bene ts of exploiting context-speci c independence.