Uncertainty in Artificial Intelligence
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A Stratified Simulation Scheme for Inference in Bayesian Belief Networks
Remco Bouckaert
Abstract:
Simulation schemes for probabilistic inference in Bayesian belief networks offer many advantages over exact algorithms; for example, these schemes have a linear and thus predictable runtime while exact algorithms have exponential runtime. Experiments have shown that likelihood weighting is one of the most promising simulation schemes. In this paper, we present a new simulation scheme that generates samples more evenly spread in the sample space than the likelihood weighting scheme. We show both theoretically and experimentally that the stratified scheme outperforms likelihood weighting in average runtime and error in estimates of beliefs.
Keywords: Bayesian belief networks, evidence propagation, simulation, stratification
Pages: 110-118
PS Link: http://www.cs.ruu.nl/people/remco/publications/UAI94.simul.ps.gz
PDF Link: /papers/94/p110-bouckaert.pdf
BibTex:
@INPROCEEDINGS{Bouckaert94,
AUTHOR = "Remco Bouckaert ",
TITLE = "A Stratified Simulation Scheme for Inference in Bayesian Belief Networks",
BOOKTITLE = "Proceedings of the Tenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-94)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1994",
PAGES = "110--118"
}


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