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# A Fully Polynomial Time Approximation Scheme for Updating Credal Networks of Bounded Treewidth and Number of Variable States

01/2011;

ABSTRACT Credal networks lift the precise probability assumption of Bayesian networks, enabling a richer representation of un-certainty in the form of closed convex sets of probabil-ity measures. The increase in expressiveness comes at the expense of higher computational costs. In this paper we present a new algorithm which is an extension of the well-known variable elimination algorithm for computing pos-terior inferences in extensively specified credal networks. The algorithm efficiency is empirically shown to outper-form a state-of-the-art algorithm. We then provide the first fully polynomial time approximation scheme for inference in credal networks with bounded treewidth and number of states per variable.

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### Keywords

convex sets

Credal networks

higher computational costs

new algorithm

outper-form

pos-terior inferences

precise probability assumption

states

well-known variable elimination algorithm