Uncertainty in Artificial Intelligence
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Hybrid Bayesian Networks with Linear Deterministic Variables
Barry Cobb, Prakash Shenoy
Abstract:
When a hybrid Bayesian network has conditionally deterministic variables with continuous parents, the joint density function for the continuous variables does not exist. Conditional linear Gaussian distributions can handle such cases when the continuous variables have a multi-variate normal distribution and the discrete variables do not have continuous parents. In this paper, operations required for performing inference with conditionally deterministic variables in hybrid Bayesian networks are developed. These methods allow inference in networks with deterministic variables where continuous variables may be non-Gaussian, and their density functions can be approximated by mixtures of truncated exponentials. There are no constraints on the placement of continuous and discrete nodes in the network.
Keywords:
Pages: 136-144
PS Link:
PDF Link: /papers/05/p136-cobb.pdf
BibTex:
@INPROCEEDINGS{Cobb05,
AUTHOR = "Barry Cobb and Prakash Shenoy",
TITLE = "Hybrid Bayesian Networks with Linear Deterministic Variables",
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 = "136--144"
}


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