Uncertainty in Artificial Intelligence
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Confidence Inference in Bayesian Networks
Jian Cheng, Marek Druzdzel
Abstract:
We present two sampling algorithms for probabilistic confidence inference in Bayesian networks. These two algorithms (we call them AIS-BN-mu and AIS-BN-sigma algorithms) guarantee 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 sampling in Bayesian networks. In addition to a simple stopping rule for sampling that they provide, the AIS-BN-mu and AIS-BN-sigma algorithms are capable of guiding the learning process in the AIS-BN algorithm. An empirical evaluation of the proposed algorithms shows excellent performance, even for very unlikely evidence.
Keywords: Stochastic sampling, Bayesian networks, error, confidence
Pages: 75-82
PS Link:
PDF Link: /papers/01/p75-cheng.pdf
BibTex:
@INPROCEEDINGS{Cheng01,
AUTHOR = "Jian Cheng and Marek Druzdzel",
TITLE = "Confidence Inference in Bayesian Networks",
BOOKTITLE = "Proceedings of the Seventeenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-01)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2001",
PAGES = "75--82"
}


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