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| DC Field | Value | Language |
|---|
| dc.contributor.author | Giang, Phan H. | - |
| dc.contributor.author | Shenoy, Prakash P. | - |
| dc.date.accessioned | 2004-12-15T19:22:50Z | - |
| dc.date.available | 2004-12-15T19:22:50Z | - |
| dc.date.issued | 2002-08 | - |
| dc.identifier.citation | Giang, P. H. and P. P. Shenoy (2002), "Statistical Decisions Using Likelihood Information Without Prior Probabilities," in A. Darwiche & N. Friedman (eds.), Uncertainty in Artificial Intelligence (UAI-02), pp. 170-178, Morgan Kaufmann, San Francisco, CA | en |
| dc.identifier.isbn | 1-55860-897-4 | - |
| dc.identifier.uri | http://hdl.handle.net/1808/157 | - |
| dc.description | This is a short 9-pp version of a longer working paper titled "Decision Making on the Sole Basis of Statistical Likelihood," School of Business Working Paper, Revised November 2004. | en |
| dc.description.abstract | This paper presents a decision-theoretic approach
to statistical inference that satisfies the Likelihood Principle (LP) without using prior information. Unlike the Bayesian approach, which also satisfies LP, we do not assume knowledge of the prior distribution of the unknown parameter. With respect to information that can be obtained from an experiment, our solution is more efficient than Waldâs minimax solution. However, with respect to information assumed to be known before the experiment, our solution demands less input than the Bayesian solution. | en |
| dc.format.extent | 305113 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.language.iso | en_US | - |
| dc.publisher | Morgan Kaufmann Publishers | en |
| dc.subject | statistical inference | en |
| dc.subject | utility theory | en |
| dc.subject | possibility theory | en |
| dc.subject | likelihood principle | en |
| dc.subject | axioms | en |
| dc.subject | decision theory | en |
| dc.title | Statistical Decisions Using Likelihood Information Without Prior Probabilities | en |
| dc.type | Book chapter | en |
| Appears in Collections: | School of Business Articles
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