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| DC Field | Value | Language |
|---|
| dc.contributor.author | Nadkarni, Sucheta | - |
| dc.contributor.author | Shenoy, Prakash P. | - |
| dc.date.accessioned | 2004-12-17T22:32:57Z | - |
| dc.date.available | 2004-12-17T22:32:57Z | - |
| dc.date.issued | 2001-02-01 | - |
| dc.identifier.citation | Nadkarni, S. and P. P. Shenoy, "A Bayesian Network Approach to Making Inferences in Causal Maps," European Journal of Operational Research, Vol. 128, No. 3, 2001, 479--498. | en |
| dc.identifier.issn | 0377-2217 | - |
| dc.identifier.uri | http://hdl.handle.net/1808/164 | - |
| dc.description.abstract | The main goal of this paper is to describe a new graphical structure called "Bayesian causal maps" to represent and analyze domain knowledge of experts. A Bayesian causal map is a causal map, i.e., a network-based representation of an expert's cognition. It is also a Bayesian network, i.e., a graphical representation of an expert's knowledge based on probability theory. Bayesian causal maps enhance the capabilities of causal maps in many ways. We describe how the textual analysis procedure for constructing causal maps can be modi®ed to construct Bayesian causal maps, and we illustrate it using a causal map of a marketing expert in the context of a product development decision. | en |
| dc.description.sponsorship | The research was supported by two University of Kansas School of Business PhD Summer Research Fund grants to both authors and by one Kansas University General Research Fund grant to Prakash P. Shenoy. | en |
| dc.format.extent | 353110 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.language.iso | en | - |
| dc.publisher | Elsevier Science Publishers B. V. | en |
| dc.subject | Causal maps | en |
| dc.subject | Cognitive Maps | en |
| dc.subject | Bayesian networks | en |
| dc.subject | Bayesian causal maps | en |
| dc.title | A Bayesian Network Approach to Making Inferences in Causal Maps | en |
| dc.type | Article | en |
| Appears in Collections: | School of Business Articles
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