Fuzzy Bayesian inference

Reinhard Viertl () and Owat Sunanta

METRON, 2013, vol. 71, issue 3, 207-216

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

Keywords: Bayesian analysis; Fuzzy data; Generalized Bayes’ theorem; Characterizing function; Vector-characterizing function; Fuzzy Bayesian analysis; Fuzzy HPD-region; Fuzzy predictive distribution (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s40300-013-0026-8

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