Evidence–Theoretical Modeling of Uncertainty Induced by Posterior Probability Distributions
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 introd...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2025-03-01
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| Series: | International Journal of Applied Mathematics and Computer Science |
| Subjects: | |
| Online Access: | https://doi.org/10.61822/amcs-2025-0003 |
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