Confidence Inference in Bayesian Networks
نویسندگان
چکیده
We present two sampling algorithms for prob abilistic confidence inference in Bayesian net works. These two algorithms (we call them AIS-BN-p and AIS-BN-li algorithms) guar antee that estimates of posterior probabilities are with a given probability within a desired precision bound. Our algorithms are based on recent advances in sampling algorithms for (1) estimating the mean of bounded random variables and (2) adaptive importance sam pling in Bayesian networks. In addition to a simple stopping rule for sampling that they provide, the AIS-BN-p and AIS-BN-0' al gorithms are capable of guiding the learning process in the AIS-BN algorithm. An em pirical evaluation of the proposed algorithms shows excellent performance, even for very unlikely evidence.
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