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| Title: | Inference in Hybrid Bayesian Networks with Deterministic Variables |
| Authors: | Cobb, Barry R.
Shenoy, Prakash P. |
| Keywords: | Hybrid Bayesian networks
Conditionally deterministic variables
Mixtures of truncated exponentials
Conditional linear Gaussian distributions |
| Issue Date: | Oct-2004 |
| Extent: | 208244 bytes |
| Type: | Book chapter |
| Citation: | Cobb, B. R. and P. P. Shenoy, "Inference in Hybrid Bayesian Networks with Deterministic Variables," in P. Lucas (ed.), Proceedings of the Second European Workshop on Probabilistic Graphical Models (PGM-04), 2004, pp. 57--64, Lorentz Center, Leiden, Netherlands. |
| Abstract: | An important class of hybrid Bayesian networks are those that have conditionally deterministic variables (a variable that is a deterministic function of its parents). In this case, if some of the parents are continuous, then the joint density function does not exist. Conditional linear Gaussian (CLG) distributions can handle such cases when the deterministic function is linear and continuous variables are normally distributed. In this paper, we develop operations required for performing inference with conditionally deterministic variables using relationships derived from joint cumulative distribution functions (CDF’s). These methods allow inference in networks with deterministic variables where continuous variables are non-Gaussian. |
| URI: | http://hdl.handle.net/1808/176 |
| Appears in Collections: | School of Business Articles
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