Bayes sharpening of imprecise information

@article{Kulczycki2005,
abstract = {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.},
author = {Kulczycki, Piotr, Charytanowicz, Małgorzata},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {nonsymmetrical loss function; numerical calculations; imprecise information; sharpening; Bayes decision rule; kernel estimators; conditioning factors; non-symmetrical loss function},
language = {eng},
number = {3},
pages = {393-404},
title = {Bayes sharpening of imprecise information},
url = {http://eudml.org/doc/207753},
volume = {15},
year = {2005},
}

TY - JOUR
AU - Kulczycki, Piotr
AU - Charytanowicz, Małgorzata
TI - Bayes sharpening of imprecise information
JO - International Journal of Applied Mathematics and Computer Science
PY - 2005
VL - 15
IS - 3
SP - 393
EP - 404
AB - 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.
LA - eng
KW - nonsymmetrical loss function; numerical calculations; imprecise information; sharpening; Bayes decision rule; kernel estimators; conditioning factors; non-symmetrical loss function
UR - http://eudml.org/doc/207753
ER -