Uncertainty in Artificial Intelligence
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Statistical Decisions Using Likelihood Information Without Prior Probabilities
Phan Giang, Prakash Shenoy
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 Wald's minimax solution.However, with respect to information assumed to be known before the experiment, our solution demands less input than the Bayesian solution.
Keywords: statistical inference, decision making with likelihoods
Pages: 170-178
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PDF Link: /papers/02/p170-giang.pdf
AUTHOR = "Phan Giang and Prakash Shenoy",
TITLE = "Statistical Decisions Using Likelihood Information Without Prior Probabilities",
BOOKTITLE = "Proceedings of the Eighteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-02)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2002",
PAGES = "170--178"

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