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Number 1 - March 2025
Volume 35 - 2025
Evidence-theoretical modeling of uncertainty induced by posterior probability distributions
Daniel Kałuża, Andrzej Janusz, Dominik Ślęzak
Abstract
We discuss how the posterior probability distributions produced by machine learning models for analyzed objects can be
transformed into evidence-theoretical mass functions that model uncertainties associated with operating those distributions.
We investigate the mathematical properties of the introduced mass functions and their corresponding belief functions.
We also construct some uncertainty measures based on the functions considered and compare them with several classical
uncertainty measures, both theoretically and practically, in the active learning scenarios.
Keywords
theory of evidence, posterior probabilities, measures of uncertainty, active learning