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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.10216 |
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| _version_ | 1866929540955111424 |
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| author | Drnevich, Matthew Jiggins, Stephen Katzy, Judith Cranmer, Kyle |
| author_facet | Drnevich, Matthew Jiggins, Stephen Katzy, Judith Cranmer, Kyle |
| contents | Motivated by real-world situations found in high energy particle physics, we consider a generalisation of the likelihood-ratio estimation task to a quasiprobabilistic setting where probability densities can be negative. By extension, this framing also applies to importance sampling in a setting where the importance weights can be negative. The presence of negative densities and negative weights, pose an array of challenges to traditional neural likelihood ratio estimation methods. We address these challenges by introducing a novel loss function. In addition, we introduce a new model architecture based on the decomposition of a likelihood ratio using signed mixture models, providing a second strategy for overcoming these challenges. Finally, we demonstrate our approach on a pedagogical example and a real-world example from particle physics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_10216 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Neural Quasiprobabilistic Likelihood Ratio Estimation with Negatively Weighted Data Drnevich, Matthew Jiggins, Stephen Katzy, Judith Cranmer, Kyle Machine Learning High Energy Physics - Experiment Motivated by real-world situations found in high energy particle physics, we consider a generalisation of the likelihood-ratio estimation task to a quasiprobabilistic setting where probability densities can be negative. By extension, this framing also applies to importance sampling in a setting where the importance weights can be negative. The presence of negative densities and negative weights, pose an array of challenges to traditional neural likelihood ratio estimation methods. We address these challenges by introducing a novel loss function. In addition, we introduce a new model architecture based on the decomposition of a likelihood ratio using signed mixture models, providing a second strategy for overcoming these challenges. Finally, we demonstrate our approach on a pedagogical example and a real-world example from particle physics. |
| title | Neural Quasiprobabilistic Likelihood Ratio Estimation with Negatively Weighted Data |
| topic | Machine Learning High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2410.10216 |