Uncertainty in Artificial Intelligence
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From Likelihood to Plausibility
Paul-Andre Monney
Abstract:
Several authors have explained that the likelihood ratio measures the strength of the evidence represented by observations in statistical problems. This idea works fine when the goal is to evaluate the strength of the available evidence for a simple hypothesis versus another simple hypothesis. However, the applicability of this idea is limited to simple hypotheses because the likelihood function is primarily defined on points (simple hypotheses) of the parameter space. In this paper we define a general weight of evidence that is applicable to both simple and composite hypotheses. It is based on the Dempster-Shafer concept of plausibility and is shown to be a generalization of the likelihood ratio. Functional models are of a fundamental importance for the general weight of evidence proposed in this paper. The relevant concepts and ideas are explained by means of a familiar urn problem and the general analysis of a real-world medical problem is presented.
Keywords: Likelihood, plausibility, Dempster-Shafer belief functions, hints, functional models,
Pages: 396-403
PS Link:
PDF Link: /papers/98/p396-monney.pdf
BibTex:
@INPROCEEDINGS{Monney98,
AUTHOR = "Paul-Andre Monney ",
TITLE = "From Likelihood to Plausibility",
BOOKTITLE = "Proceedings of the Fourteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-98)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1998",
PAGES = "396--403"
}


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