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Fuzzy Bayesian inference

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  • Reinhard Viertl
  • Owat Sunanta

Abstract

In standard Bayesian inference, a-priori distributions are assumed to be classical probability distributions. This is a topic of critical discussions because, in reality, a-priori information is usually more or less non-precise, i.e. fuzzy. Hence, a more general form of a-priori distributions (so-called fuzzy a-priori densities) is more suitable to model such a-priori information. Moreover, data from continuous quantities are always more or less fuzzy. As a result, Bayes’ theorem has to be generalized to capture this situation. This is possible and will be explained in the paper. In addition, the concepts of HPD-regions and predictive distributions are generalized to the situation of fuzzy a-priori information and fuzzy data. Copyright Sapienza Università di Roma 2013

Suggested Citation

  • Reinhard Viertl & Owat Sunanta, 2013. "Fuzzy Bayesian inference," METRON, Springer;Sapienza Università di Roma, vol. 71(3), pages 207-216, November.
    • Handle: RePEc:spr:metron:v:71:y:2013:i:3:p:207-216
    DOI: 10.1007/s40300-013-0026-8
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    Cited by:

    1. Sunanta, Owat & Viertl, Reinhard, 2016. "Fuzzy models in regional statistics," MPRA Paper 74501, University Library of Munich, Germany.

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