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A serious problem in learning probabilistic models is the presence of hidden variables. These variables are not observed, yet interact with several of the observed variables. As such, they induce seemingly complex dependencies among the latter. In recent years, much attention has been devoted to the development of algorithms for learning parameters, and in(More)
I n this paper, we use evidence-specific value ab­ straction for speeding Bayesian networks infer­ ence. This is done by grouping variable val­ ues and treating the combined values as a sin­ gle entity. As we show, such abstractions can ex­ ploit regularities in conditional probability distri­ butions and also the specific values of observed variables. To(More)
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