Despite its name,

imprecise probability theory is more complete and accurate than precise probability in the real world where uncertainty prevails.

Computer scientists and statisticians introduce graduate students, researchers, and consultants to

imprecise probability, a new framework for quantifying uncertainty and making inferences and decisions under it.

Also, he suggested an extension of the classical probability and

imprecise probability to "neutrosophic probability".

Despite its name, imprecise probability is more complete and accurate than precise probability in the real world where probabilistic imprecision prevails.

13) Imprecise probability goes back to the nineteenth century.

In many systems (technical, economical or social) it is impossible to precisely calculate the relation, so we deal with the problem of

imprecise probability.

1996) stated that "Many people believe that assigning an exact number to an expert's opinion is too restrictive, and the assignment of an interval of values is more realistic", which is somehow similar with the imprecise probability theory where instead of a crisp probability one has an interval (upper and lower) probabilities as in Walley (1991).

Walley (1991), Statistical Reasoning with Imprecise Probability, Chapman/Hall.

Fuzzy probability is characterized by a possibility distribution of probability, which represents an

imprecise probability by means of a subjective possibility measure associated with judgmental uncertainty.

Subjects in the

imprecise probability manipulation had the same information as above, except that the information on the probability of the process being out of control was stated as follows:

Belief functions thus harness the idea of

imprecise probability to capture indeterminacy.

It is also a generalization of the

imprecise probability, which is an interval-valued distribution function.