Bayesian decision making using z-numbers

Abstract

Bayesian decision methods have been widely used in different areas for estimating, predicting, and offering decision supports. On the other hand, computing with words (CW) and approximate reasoning have effectively been used to deal with imprecise measurements and inexact information. This work is an attempt to benefit from both approaches and to combine them to model the expert knowledge in natural language and apply it to Bayesian decision support systems. The proposed method employs z-numbers to represent the knowledge in natural language and its certainty (reliability). To evaluate applicability of the proposed method, an example is provided as a proof of concept.