Bayes sharpening of imprecise information

• Autorzy: Piotr Kulczycki , Małgorzata Charytanowicz
• Języki publikacji: EN

Abstrakty

EN

A complete algorithm is presented for the sharpening of imprecise information, based on the methodology of kernel estimators and the Bayes decision rule, including conditioning factors. The use of the Bayes rule with a nonsymmetrical loss function enables the inclusion of different results of an under- and overestimation of a sharp value (real number), as well as minimizing potential losses. A conditional approach allows to obtain a more precise result thanks to using information entered as the assumed (e.g. current) values of conditioning factors of continuous andor binary types. The nonparametric methodology of statistical kernel estimators freed the investigated procedure from arbitrary assumptions concerning the forms of distributions characterizing both imprecise information and conditioning random variables. The concept presented here is universal and can be applied in a wide range of tasks in contemporary engineering, economics, and medicine.

Słowa kluczowe

EN

nonsymmetrical loss function; numerical calculations; imprecise information; sharpening; Bayes decision rule; kernel estimators; conditioning factors

• Wydawca: University of Zielona Gora Press
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2005
• Tom: 15
• Numer: 3
• Strony: 393-404

Daty

wydano

2005

otrzymano

2005-04-25

poprawiono

2005-06-30

Twórcy

autor

Piotr Kulczycki

• Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01-447 Warsaw, Poland

autor

Małgorzata Charytanowicz

• Institute of Mathematics, Catholic University of Lublin, Al. Racławickie 14, 20-950 Lublin, Poland

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