Evidence Theory Approach and Bayesian Approach for Modeling Uncertainty when Information is Imprecise

This paper investigates the potential of Evidence Theory (ET) and Bayesian Theory (BT) for decision under uncertainty, when the evidence about uncertainty is imprecise. The basic concepts of ET and BT are introduced and the ways these theories model uncertainties, propagate them through systems and assess the safety of these systems are presented. ET and BT approaches are demonstrated and compared on examples involving an algebraic function when the evidence about the input variables consists of intervals provided by experts. It is recommended that a decision maker compute both the Bayesian probability of events and their lower and upper probabilities using ET when evidence from experts is imprecise. A large gap between the lower and upper probability suggests that more information should be collected before making a decision. If this is not feasible, then Bayesian probabilities can help make a decision.