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Languages and Designs for Probability Judgment

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

Abstract

Theories of subjective probability are viewed as formal languages for analyzing evidence and expressing degrees of belief. This article focuses on two probability language, the Bayesian language and the language of belief functions [199]. We describe and compare the semantics (i.e., the meaning of the scale) and the syntax (i.e., the formal calculus) of these languages. We also investigate some of the designs for probability judgment afforded by the two languages.

Keywords

  • Subjective Probability
  • Belief Function
  • Total Evidence
  • Mental Experiment
  • Probability Judgment

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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Shafer, G., Tversky, A. (2008). Languages and Designs for Probability Judgment. 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_13

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  • DOI: https://doi.org/10.1007/978-3-540-44792-4_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25381-5

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