Simulation Approaches to General Probabilistic Inference on Belief Networks

27 Mar 2013 Ross D. Shachter Mark Alan Peot

A number of algorithms have been developed to solve probabilistic inference problems on belief networks. These algorithms can be divided into two main groups: exact techniques which exploit the conditional independence revealed when the graph structure is relatively sparse, and probabilistic sampling techniques which exploit the "conductance" of an embedded Markov chain when the conditional probabilities have non-extreme values... (read more)

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