Bayesian Confirmation Measures (BCMs) assess the impact of the occurrence of one event on the credibility of another. Many mea- sures of this kind have been defined in literature. We want to analyze how these measures change when the probabilities involved in their computation are distorted. Composing distortions and BCMs we define a set of Distorted Bayesian Confirmation Measures (DBCMs); we study the properties that DBCMs may inherit from BCMs, and propose a way to measure the degree of distortion of a DBCM with respect to a corre- sponding BCM.
Autori: | Ferretti, Paola (Corresponding) | |
Data di pubblicazione: | 2020 | |
Titolo: | Distorted Probabilities and Bayesian Confirmation Measures | |
Titolo del libro: | Modeling Decisions for Artificial Intelligence: 17th International Conference, MDAI 2020, Sant Cugat, Spain, September 2-4, 2020 | |
Digital Object Identifier (DOI): | http://dx.doi.org/10.1007/978-3-030-57524-3_8 | |
Appare nelle tipologie: | 3.1 Articolo su libro |
File in questo prodotto:
File | Descrizione | Tipologia | Licenza | |
---|---|---|---|---|
paper_Ellero_Ferretti MDAI 2020.pdf | Documento in Pre-print | Accesso chiuso-personale | Riservato |
I documenti in ARCA sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.