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| 可能性理论× | 不精确概率× | |
|---|---|---|
| 领域 | 软计算 | 软计算 |
| 方法族≠ | Machine learning | Bayesian methods |
| 起源年份≠ | 1988 | 1991 |
| 提出者≠ | Lotfi Zadeh; Didier Dubois & Henri Prade | Peter Walley |
| 类型≠ | Uncertainty quantification framework | Set-valued probability model |
| 开创性文献≠ | Dubois, D., & Prade, H. (1988). Possibility Theory: An Approach to Computerized Processing of Uncertainty. Plenum Press. ISBN: 978-0-306-42520-2 | Walley, P. (1991). Statistical Reasoning with Imprecise Probabilities. Chapman & Hall. ISBN: 978-0-412-28660-5 |
| 别名 | Fuzzy Possibility Theory, Possibilistic Reasoning, Olasılık Teorisi (Bulanık), Possibility Distribution Theory | Lower-Upper Probability, Robust Bayesian Analysis, Credal Set Theory, Belirsiz Olasılık |
| 相关 | 3 | 3 |
| 摘要≠ | 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. | 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. |
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