Uncertainty in Artificial Intelligence
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Adaptive Importance Sampling for Estimation in Structured Domains
Luis Ortiz, Leslie Kaelbling
Sampling is an important tool for estimating large, complex sums and integrals over high dimensional spaces. For instance, important sampling has been used as an alternative to exact methods for inference in belief networks. Ideally, we want to have a sampling distribution that provides optimal-variance estimators. In this paper, we present methods that improve the sampling distribution by systematically adapting it as we obtain information from the samples. We present a stochastic-gradient-descent method for sequentially updating the sampling distribution based on the direct minization of the variance. We also present other stochastic-gradient-descent methods based on the minimizationof typical notions of distance between the current sampling distribution and approximations of the target, optimal distribution. We finally validate and compare the different methods empirically by applying them to the problem of action evaluation in influence diagrams.
Pages: 446-454
PS Link:
PDF Link: /papers/00/p446-ortiz.pdf
AUTHOR = "Luis Ortiz and Leslie Kaelbling",
TITLE = "Adaptive Importance Sampling for Estimation in Structured Domains",
BOOKTITLE = "Proceedings of the Sixteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-00)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2000",
PAGES = "446--454"

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