Imprecise Probabilities from Imprecise Descriptions of Real Numbers

Abstract

A prototype theory interpretation of the label semantics framework is proposed as a possible model of imprecise descriptions of real numbers. It is shown that within this framework conditioning given imprecise descriptions of a real variable naturally results in imprecise probabilities. An inference method is proposed from data in the form of a set of imprecise descriptions,which naturally suggests an algorithm for estimating lower and upper probabilities given imprecise data values.A prototype theory interpretation of the label semantics framework is proposed as a possible model of imprecise descriptions of real numbers. It is shown that within this framework conditioning given imprecise descriptions of a real variable naturally results in imprecise probabilities. An inference method is proposed from data in the form of a set of imprecise descriptions,which naturally suggests an algorithm for estimating lower and upper probabilities given imprecise data values