Generative Models and Statistical Validation
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866910270722408448 |
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| author | Diefenbacher, Sascha Schweitzer, Sofia Palacios Kasieczka, Gregor |
| author_facet | Diefenbacher, Sascha Schweitzer, Sofia Palacios Kasieczka, Gregor |
| contents | Generative machine learning has become an essential tool in theoretical and experimental physics, especially in the context of fast surrogates and density estimators. In this work, we first introduce the underlying framework of modern generative networks and then discuss challenges in quantifying their accuracy, precision, and statistical power. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_30453 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Generative Models and Statistical Validation Diefenbacher, Sascha Schweitzer, Sofia Palacios Kasieczka, Gregor High Energy Physics - Phenomenology Machine Learning Data Analysis, Statistics and Probability Generative machine learning has become an essential tool in theoretical and experimental physics, especially in the context of fast surrogates and density estimators. In this work, we first introduce the underlying framework of modern generative networks and then discuss challenges in quantifying their accuracy, precision, and statistical power. |
| title | Generative Models and Statistical Validation |
| topic | High Energy Physics - Phenomenology Machine Learning Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2605.30453 |