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    Statistical Decisions Using Likelihood Information Without Prior Probabilities

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    UAI02.pdf (297.9Kb)
    Issue Date
    2002-08
    Author
    Giang, Phan H.
    Shenoy, Prakash P.
    Publisher
    Morgan Kaufmann Publishers
    Format
    305113 bytes
    Type
    Book chapter
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    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 Waldâ s minimax solution. However, with respect to information assumed to be known before the experiment, our solution demands less input than the Bayesian solution.
    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.
    URI
    http://hdl.handle.net/1808/157
    ISBN
    1-55860-897-4
    Collections
    • Distinguished Professors Scholarly Works [918]
    • School of Business Scholarly Works [216]
    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

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    Lawrence, KS 66045
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    Contact KU ScholarWorks
    785-864-8983
    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    785-864-8983

    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    Image Credits
     

     

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