Uncertainty in Artificial Intelligence
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A Randomized Approximation Algorithm of Logic Sampling
R. Chavez, Gregory Cooper
Abstract:
In recent years, researchers in decision analysis and artificial intelligence (AI) have used Bayesian belief networks to build models of expert opinion. Using standard methods drawn from the theory of computational complexity, workers in the field have shown that the problem of exact probabilistic inference on belief networks almost certainly requires exponential computation in the worst ease [3]. We have previously described a randomized approximation scheme, called BN-RAS, for computation on belief networks [ 1, 2, 4]. We gave precise analytic bounds on the convergence of BN-RAS and showed how to trade running time for accuracy in the evaluation of posterior marginal probabilities. We now extend our previous results and demonstrate the generality of our framework by applying similar mathematical techniques to the analysis of convergence for logic sampling [7], an alternative simulation algorithm for probabilistic inference.
Keywords:
Pages: 130-135
PS Link:
PDF Link: /papers/90/p130-chavez.pdf
BibTex:
@INPROCEEDINGS{Chavez90,
AUTHOR = "R. Chavez and Gregory Cooper",
TITLE = "A Randomized Approximation Algorithm of Logic Sampling",
BOOKTITLE = "Proceedings of the Sixth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-90)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "1990",
PAGES = "130--135"
}


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