Upper and lower probabilities are representations of imprecise probability. Whereas probability theory uses a single number, the probability, to describe how likely an event is to occur, this method uses two numbers: the upper probability of the event and the lower probability of the event.
Because frequentist statistics disallows metaprobabilities, frequentists have had to propose new solutions. Cedric Smith and Arthur Dempster each developed a theory of upper and lower probabilities. Glenn Shafer developed Dempster's theory further, and it is now known as Dempster–Shafer theory or Choquet (1953).
More precisely, in the work of these authors one considers in a power set, , a mass function satisfying the conditions
In turn, a mass is associated with two non-additive continuous measures called belief and plausibility defined as follows:
In the case where is infinite there can be such that there is no associated mass function. See p. 36 of Halpern (2003). Probability measures are a special case of belief functions in which the mass function assigns positive mass to singletons of the event space only.
A different notion of upper and lower probabilities is obtained by the lower and upper envelopes obtained from a class C of probability distributions by setting
The upper and lower probabilities are also related with probabilistic logic: see Gerla (1994).
Observe also that a necessity measure can be seen as a lower probability and a possibility measure can be seen as an upper probability.
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En mathématiques et en informatique, la théorie des possibilités est une alternative à la théorie des probabilités pour représenter l'incertitude. Lotfi Zadeh a d'abord introduit la théorie des possibilités en 1978 comme une extension de sa théorie des ensembles flous et la logique floue. Didier Dubois et Henri Prade ont ensuite contribué à son développement. Étant donné un univers Ω que l'on suppose fini pour simplifier la présentation, une mesure ou distribution de possibilité est une fonction de dans [0, 1], c'est-à-dire à chaque sous-ensemble d'événements U, on associe pos(U) qui mesure la possibilité de U.
Imprecise probability generalizes probability theory to allow for partial probability specifications, and is applicable when information is scarce, vague, or conflicting, in which case a unique probability distribution may be hard to identify. Thereby, the theory aims to represent the available knowledge more accurately. Imprecision is useful for dealing with expert elicitation, because: People have a limited ability to determine their own subjective probabilities and might find that they can only provide an interval.