]> 2012-02-11T04:09:34-05:00 A Bayesian Analysis of Simulation Algorithms for Inference in Belief Networks Belief networks are an expressive representation for encoding expert knowledge about uncertain causal relationships. Both exact and approximation methods for performing inference with belief networks can pose difficult computational problems in the worst case. Nevertheless, approximation procedures hold promise to provide estimates efficiently for a variety of complex networks that resist exact solution. We characterize the performance of algorithms in the important class of inference procedures based on stochastic simulation. We develop terms for the error associated with estimates generated by several simulation methods, including forward simulation, likelihood weighting, and randomized approximation strategies. techreport 1993 A Bayesian Analysis of Simulation Algorithms for Inference in Belief Networks KSL-91-67 1993 September. A Bayesian Analysis of Simulation Algorithms for Inference in Belief Networks 1993 Year Title YES Process note Number Note Ksl tr id Knowledge Systems, AI Laboratory Institution Has year Has where published Has title Has publishing details Has identifier Has author Bibtype Paul Dagum and Eric Horvitz Author Abstract KSL Technical Report Publication Technical Report