Loading...
Hybrid Bayesian Networks with Linear Deterministic Variables
Cobb, Barry R. Shenoy, Prakash P.
Cobb, Barry R.
Shenoy, Prakash P.
Citations
Altmetric:
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.
Description
Date
2005-07
Journal Title
Journal ISSN
Volume Title
Publisher
Association for Uncertainty in Artificial Intelligence
Files
Loading...
UAI05.pdf
Adobe PDF, 492.38 KB
Research Projects
Organizational Units
Journal Issue
Keywords
Hybrid Bayesian networks, Conditionally deterministic variables, Linear deterministic functions, Shenoy-Shafer architecture
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.