Numerical Methods for Computing Plausibility and Belief Distributions of Consequences of a Subjective Model of Object of Research

Abstract

Numerical methods for computing plausibility and belief distributions of consequences of a subjective model are considered. More precisely, related constrained optimization problems are studied. Error estimates of the proposed algorithms are obtained. Techniques for taking into account the information about the consequence available to the researcher for improving the accuracy of computations are discussed.