Scientific Reports:
R. Viertl, O. Sunanta:
"Fuzzy Bayesian inference";
Report No. Forschungsbericht SM-2013-2 Juni,
2013;
11 pages.
English 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
Keywords:
Bayesian Analysis ˇ Fuzzy Data ˇ Generalized Bayes´ Theorem ˇ Characterizing Function ˇ Vector-characterizing Function ˇ Fuzzy Bayesian Analysis ˇ Fuzzy HPD-Region ˇ Fuzzy Predictive Distribution
Electronic version of the publication:
http://www.statistik.tuwien.ac.at/forschung/SM/SM-2013-2complete.pdf
Created from the Publication Database of the Vienna University of Technology.