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.

Distorted Probabilities and Bayesian Confirmation Measures

Ellero, Andrea
;
Ferretti, Paola
2020

Abstract

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.
Modeling Decisions for Artificial Intelligence: 17th International Conference, MDAI 2020, Sant Cugat, Spain, September 2-4, 2020
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/3729496
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