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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.24735 |
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| _version_ | 1866917070199848960 |
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| author | Trésor, Raphaël Lukashchuk, Mykola |
| author_facet | Trésor, Raphaël Lukashchuk, Mykola |
| contents | This paper presents a rigorous resolution of the Borel-Kolmogorov paradox using the Maximum Entropy Principle. We construct a metric-based framework for Bayesian inference that uniquely extends conditional probability to events of null measure. The results unify classical Bayes' rules and provide a robust foundation for Bayesian inference in metric spaces. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_24735 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Resolution of the Borel-Kolmogorov Paradox via the Maximum Entropy Principle Trésor, Raphaël Lukashchuk, Mykola Statistics Theory This paper presents a rigorous resolution of the Borel-Kolmogorov paradox using the Maximum Entropy Principle. We construct a metric-based framework for Bayesian inference that uniquely extends conditional probability to events of null measure. The results unify classical Bayes' rules and provide a robust foundation for Bayesian inference in metric spaces. |
| title | Resolution of the Borel-Kolmogorov Paradox via the Maximum Entropy Principle |
| topic | Statistics Theory |
| url | https://arxiv.org/abs/2509.24735 |