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A Generalization of Bayesian Inference

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Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ,volume 219)

Abstract

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.

Keywords

  • Lower Probability
  • Bayesian Inference
  • Prior Information
  • Uncertainty Principle
  • Product Rule

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© 2008 Springer-Verlag Berlin Heidelberg

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Dempster, A.P. (2008). A Generalization of Bayesian Inference. In: Yager, R.R., Liu, L. (eds) Classic Works of the Dempster-Shafer Theory of Belief Functions. Studies in Fuzziness and Soft Computing, vol 219. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-44792-4_4

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  • DOI: https://doi.org/10.1007/978-3-540-44792-4_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25381-5

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