Uncertainty in Artificial Intelligence
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Evidence with Uncertain Likelihoods
Joseph Halpern, Riccardo Pucella
An agent often has a number of hypotheses, and must choose among them based on observations, or outcomes of experiments. Each of these observations can be viewed as providing evidence for or against various hypotheses. All the attempts to formalize this intuition up to now have assumed that associated with each hypothesis h there is a likelihood function μh, which is a probability measure that intuitively describes how likely each observation is, conditional on h being the correct hypothesis. We consider an extension of this framework where there is uncertainty as to which of a number of likelihood functions is appropriate, and discuss how one formal approach to defining evidence, which views evidence as a function from priors to posteriors, can be generalized to accommodate this uncertainty.
Pages: 243-250
PS Link:
PDF Link: /papers/05/p243-halpern.pdf
AUTHOR = "Joseph Halpern and Riccardo Pucella",
TITLE = "Evidence with Uncertain Likelihoods",
BOOKTITLE = "Proceedings of the Twenty-First Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-05)",
ADDRESS = "Arlington, Virginia",
YEAR = "2005",
PAGES = "243--250"

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