A Generalization of Bayesian Inference

  • Arthur P. Dempster
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 219)


Procedures of statistical inference are described which generalize Bayesian inference in specific ways. Probability is used in such a way that in general only bounds may be placed on the probabilities of given events, and probability systems of this kind are suggested both for sample information and for prior information. These systems are then combined using a specified rule. Illustrations are given for inferences about trinomial probabilities, and for inferences about a monotone sequence of binomial pi. Finally, some comments are made on the general class of models which produce upper and lower probabilities, and on the specific models which underlie the suggested inference procedures.


Lower Probability Bayesian Inference Prior Information Uncertainty Principle Product Rule 
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© Springer-Verlag Berlin Heidelberg 2008

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  • Arthur P. Dempster

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