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The Self-Importance Sampling algorithm is described in (Shachter & Peot 1990). It controls the generation of samples to account for the bias due to disproportional sampling of cases that represent the most probable hypotheses. This algorithm may have been the first algorithm using the concept of importance sampling in Bayesian network algorithms.