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Please use this identifier to cite or link to this item: http://hdl.handle.net/1808/520
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Title: Hybrid Bayesian Networks with Linear Deterministic Variables
Authors: Cobb, Barry R.
Shenoy, Prakash P.
Keywords: Hybrid Bayesian networks
Conditionally deterministic variables
Linear Deterministic Functions
Shenoy-Shafer Architecture
Issue Date: Jul-2005
Publisher: Association for Uncertainty in Artificial Intelligence
Extent: 504200 bytes
Type: Book chapter
Citation: Cobb, B. R. and P. P. Shenoy (2005), "Hybrid Bayesian networks with linear deterministic variables," in F. Bacchus and T. Jaakkola (eds.), Uncertainty in Artificial Intelligence: Proceedings of the Twenty-First Conference (UAI-05), pp. 136--144, AUAI Press, Corvallis, OR.
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.
URI: http://hdl.handle.net/1808/520
ISBN: 0-9749039-1-4
Appears in Collections:School of Business Articles

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