Sequential Updating Conditional Probability in Bayesian Networks by Posterior Probability
نویسندگان
چکیده
The Bayesian network is a powerful knowledge representation formalism; it is also capable of improving its precision through experience. Spiegelhalter et al. [1989] proposed a procedure for sequential updating forward conditional probabilities (FCP) in Bayesian networks of diameter 1 with a single parent node. The procedure assumes certainty for each diagnosis which is not practical for many applications. In this paper we present a new algorithm (ALPP) that allows refinement of FCPs based on expert estimates of posterior probability. ALPP applies to any DAG of diameter 1. Fast convergence is achieved. Simulation results compare ALPP with Spiegelhalter’s method.
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