Algorithms for Approximate Probability Propagation in Bayesian Networks

When a Bayesian network is defined over a very large or complicated domain, computing the posterior probabilities given some evidence may become unfeasible. In fact, probability propagation is known to be an NP-hard problem. Since it is common to find huge domains in practical applications, approximate algorithms have been developed. These algorithms compute estimations of the posterior probabilities with lower requirements, in terms of memory and computing time, than exact algorithms. In this paper we present some of the most recent developments in the area of approximate propagation algorithms.

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