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| DC Field | Value | Language |
|---|
| dc.contributor.author | Cobb, Barry R. | - |
| dc.contributor.author | Shenoy, Prakash P. | - |
| dc.date.accessioned | 2004-12-14T20:41:46Z | - |
| dc.date.available | 2004-12-14T20:41:46Z | - |
| dc.date.issued | 2004-07 | - |
| dc.identifier.citation | M. Chickering and J. Halpern (eds.), Uncertainty in Artificial Intelligence (UAI-04), 2004, pp. 85--93, AUAI Press, Arlington, VA | en |
| dc.identifier.isbn | 0-9749039-0-6 | - |
| dc.identifier.uri | http://hdl.handle.net/1808/151 | - |
| dc.description | This is a short 9-pp version of a longer un-published working paper titled "Decision Making with Hybrid Influence Diagrams Using Mixtures of Truncated Exponentials," School of Business Working Paper No. 304, May 2004, Lawrence, KS. | en |
| dc.description.abstract | Mixtures of truncated exponentials (MTE) potentials are an alternative to discretization
for representing continuous chance variables in influence diagrams. Also, MTE potentials can be used to approximate utility functions. This paper introduces MTE influence diagrams, which can represent decision problems without restrictions on the relationships between continuous and discrete chance variables, without limitations on the distributions of continuous chance variables, and without limitations on the nature of the utility functions. In MTE influence diagrams, all probability distributions and the joint utility function (or its multiplicative factors) are represented by MTE potentials and decision nodes are assumed to have discrete state spaces. MTE
influence diagrams are solved by variable elimination using a fusion algorithm. | en |
| dc.description.sponsorship | Partially supported by a graduate research assistantship to Barry R. Cobb from the Ronald G. Harper Professorship, and by a contract from Sparta, Inc., to Prakash P. Shenoy. | en |
| dc.format.extent | 279654 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.language.iso | en_US | - |
| dc.publisher | Association for Uncertainty in Artificial Intelligence | en |
| dc.subject | Influence diagrams | en |
| dc.subject | Hybrid Bayesian networks | en |
| dc.subject | Mixtures of truncated exponentials | en |
| dc.title | Hybrid Influence Diagrams Using Mixtures of Truncated Exponentials | en |
| dc.type | Book chapter | en |
| Appears in Collections: | School of Business Articles
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