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 |
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Sciendo
2025-03-01
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| Series: | International Journal of Applied Mathematics and Computer Science |
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| Online Access: | https://doi.org/10.61822/amcs-2025-0003 |
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| _version_ | 1850216847534718976 |
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| author | Kałuża Daniel Janusz Andrzej Ślęzak Dominik |
| author_facet | Kałuża Daniel Janusz Andrzej Ślęzak Dominik |
| author_sort | Kałuża Daniel |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-0967b3ecbbe04ba0ba96211ee77fca63 |
| institution | OA Journals |
| issn | 2083-8492 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Sciendo |
| record_format | Article |
| series | International Journal of Applied Mathematics and Computer Science |
| spelling | doaj-art-0967b3ecbbe04ba0ba96211ee77fca632025-08-20T02:08:12ZengSciendoInternational Journal of Applied Mathematics and Computer Science2083-84922025-03-01351334310.61822/amcs-2025-0003Evidence–Theoretical Modeling of Uncertainty Induced by Posterior Probability DistributionsKałuża Daniel0Janusz Andrzej1Ślęzak Dominik21Institute of InformaticsUniversity of Warsawul. Banacha 2, 02-097Warsaw, Poland1Institute of InformaticsUniversity of Warsawul. Banacha 2, 02-097Warsaw, Poland1Institute of InformaticsUniversity of Warsawul. Banacha 2, 02-097Warsaw, PolandWe 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.https://doi.org/10.61822/amcs-2025-0003theory of evidenceposterior probabilitiesmeasures of uncertaintyactive learning |
| spellingShingle | Kałuża Daniel Janusz Andrzej Ślęzak Dominik Evidence–Theoretical Modeling of Uncertainty Induced by Posterior Probability Distributions International Journal of Applied Mathematics and Computer Science theory of evidence posterior probabilities measures of uncertainty active learning |
| title | Evidence–Theoretical Modeling of Uncertainty Induced by Posterior Probability Distributions |
| title_full | Evidence–Theoretical Modeling of Uncertainty Induced by Posterior Probability Distributions |
| title_fullStr | Evidence–Theoretical Modeling of Uncertainty Induced by Posterior Probability Distributions |
| title_full_unstemmed | Evidence–Theoretical Modeling of Uncertainty Induced by Posterior Probability Distributions |
| title_short | Evidence–Theoretical Modeling of Uncertainty Induced by Posterior Probability Distributions |
| title_sort | evidence theoretical modeling of uncertainty induced by posterior probability distributions |
| topic | theory of evidence posterior probabilities measures of uncertainty active learning |
| url | https://doi.org/10.61822/amcs-2025-0003 |
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