At second phase, the acquired results of previous stage are assumed as basic beliefs for the problem propositions and in this way, the belief and plausibility functions (or the belief interval) are set.
The average distance between the belief interval of each singleton and the most uncertain case is used to represent the total uncertainty degree of the given body of evidence.
The belief interval represents a range in which true probability may lie.
The belief interval represents a range where the probability may lie.
The interval that has a great belief interval will be chosen since the belief function determines a low probability that is gained through the minimum available data.
As the approach above mentioned, the similar belief interval as [Spt S m |y(t)), Pls(S m | y(t))] can be achieved for each y ( t ), t = 1, 2, …, T ̄.
Three decision support indicators including plausibility, belief interval and belief were generated (see Figs. 5 7).
First, the belief-moving effect of apparently irrelevant evidence; and second, the fact that learning some evidence can cause your belief-intervals to widen.
Many multiple attribute decision analysis problems include both quantitative and qualitative attributes with various kinds of uncertainties such as ignorance, fuzziness, interval data, and interval belief degrees.
Seemingly imbued with belief after the interval, Darren Fletcher put Miller through one on one with the goalkeeper, only for his shot to be saved easily, rebounding back to Fletcher.
An unsolved problem in the interval-valued belief structure is the existence of maximal confidence intervals, referring to the credible and maximal interval-valued beliefs assigned to focal elements, in which every point can be reached by a combination rule.
