1. Statistical Reasoning with Imprecise Probabilities.Peter Walley - 1991 - Chapman & Hall.
    An examination of topics involved in statistical reasoning with imprecise probabilities. The book discusses assessment and elicitation, extensions, envelopes and decisions, the importance of imprecision, conditional previsions and coherent statistical models.
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    Measures of uncertainty in expert systems.Peter Walley - 1996 - Artificial Intelligence 83 (1):1-58.
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    Inferences from Multinomal Data: Learning about a bag of marbles (with discussion).Peter Walley - 1996 - Journal of the Royal Statistical Society Series B 58:3-57.
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    Varieties of modal (classificatory) and comparative probability.Peter Walley & Terrence L. Fine - 1979 - Synthese 41 (3):321 - 374.
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    A possibilistic hierarchical model for behaviour under uncertainty.Gert de Cooman & Peter Walley - 2002 - Theory and Decision 52 (4):327-374.
    Hierarchical models are commonly used for modelling uncertainty. They arise whenever there is a `correct' or `ideal' uncertainty model but the modeller is uncertain about what it is. Hierarchical models which involve probability distributions are widely used in Bayesian inference. Alternative models which involve possibility distributions have been proposed by several authors, but these models do not have a clear operational meaning. This paper describes a new hierarchical model which is mathematically equivalent to some of the earlier, possibilistic models and (...)
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