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Abstract: The monitoring and control of any dynamic system depends crucially on the ability to reason about its current status and its future trajectory. In the case of a stochastic system, these tasks typically involve the use of a belief state---a probability distribution over the state of the process at a given point in time. Unfortunately, the state spaces of complex processes are very large, making an explicit representation of a belief state intractable. Even in dynamic Bayesian networks (DBNs),... (Update)
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BibTeX entry: (Update)
X. Boyen and D. Koller. Tractable inference for complex stochastic processes. In Proc. of the Conf. on Uncertainty in AI, 1998. http://citeseer.ist.psu.edu/boyen98tractable.html More
@inproceedings{ boyentractable,
author = "Xavier Boyen and Daphne Koller",
title = "Tractable Inference for Complex Stochastic Processes",
pages = "33--42",
url = "citeseer.ist.psu.edu/boyen98tractable.html" }
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