Uncertainty in Artificial Intelligence
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Approximate Inference Algorithms for Hybrid Bayesian Networks with Discrete Constraints
Vibhav Gogate, Rina Dechter
Abstract:
In this paper, we consider Hybrid Mixed Networks (HMN) which are Hybrid Bayesian Networks that allow discrete deterministic information to be modeled explicitly in the form of constraints. We present two approximate inference algorithms for HMNs that integrate and adjust well known algorithmic principles such as Generalized Belief Propagation, Rao-Blackwellised Importance Sampling and Constraint Propagation to address the complexity of modeling and reasoning in HMNs. We demonstrate the performance of our approximate inference algorithms on randomly generated HMNs.
Keywords:
Pages: 209-216
PS Link:
PDF Link: /papers/05/p209-gogate.pdf
BibTex:
@INPROCEEDINGS{Gogate05,
AUTHOR = "Vibhav Gogate and Rina Dechter",
TITLE = "Approximate Inference Algorithms for Hybrid Bayesian Networks with Discrete Constraints",
BOOKTITLE = "Proceedings of the Twenty-First Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-05)",
PUBLISHER = "AUAI Press",
ADDRESS = "Arlington, Virginia",
YEAR = "2005",
PAGES = "209--216"
}


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