Beliefs result from uncertainty. Uncertainty sometimes results from a random process and sometimes from a lack of information. In the past, the only solution in situations of uncertainty was probability theory. However, over the past few decades, various other theories have been proposed to study variables and systems about which information is insufficient and inaccurate. One of these solutions is the theory of belief functions or the Dempster-Schaffner theory. This theory has been considered as a generalization of probability theory that allows for the representation of different states of information, from complete certainty to complete ignorance. The belief function provides a way to use mathematical probability in subjective judgments. One model for representing belief functions is the belief transfer model. This model is conceptually similar to the Bayes model. In this model, beliefs are at two levels, one is the belief level at which beliefs are accepted and measured by the belief function, and the other is the betting level at which beliefs can be used for decision-making and measured by probability functions. The difference between this model and the Bayesian model is the presence of a belief level. The Bayesian model does not have this level.