| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 4945279 | International Journal of Approximate Reasoning | 2017 | 20 Pages |
Abstract
Likelihood functions are studied in a probabilistic and possibilistic setting: inferential conclusions are drawn from a set of likelihood functions and prior information relying on the notion of disintegrability. The present study allows for a new interpretation of fuzzy membership functions as coherent conditional possibilities. The concept of possibility of a fuzzy event is then introduced and a comparison with the probability of a fuzzy event is provided.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Giulianella Coletti, Davide Petturiti, Barbara Vantaggi,