Publications in Scientific Journals:
R. Viertl, O. Sunanta:
"Fuzzy Bayesian inference";
METRON,
Volume 71
(2013),
Issue 3;
207
- 216.
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.
"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)
http://dx.doi.org/10.1007/s40300-013-0026-8
Electronic version of the publication:
http://link.springer.com/article/10.1007/s40300-013-0026-8
Created from the Publication Database of the Vienna University of Technology.