Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Нечёткая вероятность× | Теория возможности× | |
|---|---|---|
| Область | Мягкие вычисления | Мягкие вычисления |
| Семейство≠ | Bayesian methods | Machine learning |
| Год появления≠ | 1991 | 1988 |
| Автор метода≠ | Peter Walley | Lotfi Zadeh; Didier Dubois & Henri Prade |
| Тип≠ | Set-valued probability model | Uncertainty quantification framework |
| Основополагающий источник≠ | Walley, P. (1991). Statistical Reasoning with Imprecise Probabilities. Chapman & Hall. ISBN: 978-0-412-28660-5 | Dubois, D., & Prade, H. (1988). Possibility Theory: An Approach to Computerized Processing of Uncertainty. Plenum Press. ISBN: 978-0-306-42520-2 |
| Другие названия | Lower-Upper Probability, Robust Bayesian Analysis, Credal Set Theory, Belirsiz Olasılık | Fuzzy Possibility Theory, Possibilistic Reasoning, Olasılık Teorisi (Bulanık), Possibility Distribution Theory |
| Связанные | 3 | 3 |
| Сводка≠ | Imprecise probability is a generalization of standard probability theory that represents epistemic uncertainty through sets of probability measures, called credal sets, rather than a single precise distribution. Introduced systematically by Peter Walley in his 1991 monograph, the framework characterizes beliefs via lower and upper probabilities (or previsions), bracketing the range of plausible probability assignments when available information is insufficient to determine a unique measure. | Possibility Theory is a mathematical framework for representing and reasoning under uncertainty, introduced by Lotfi Zadeh in 1978 and systematically developed by Didier Dubois and Henri Prade in their 1988 monograph. It uses possibility distributions — functions assigning a degree in [0,1] to each element of a universe — to encode what is plausible or consistent with available information, complementing probability theory for situations where data is scarce or knowledge is imprecise. |
| ScholarGateНабор данных ↗ |
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