Recent advances in the Bradley--Terry Model: theory, algorithms, and applications
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866909997521174528 |
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| author | Fang, Shuxing Han, Ruijian Luo, Yuanhang Xu, Yiming |
| author_facet | Fang, Shuxing Han, Ruijian Luo, Yuanhang Xu, Yiming |
| contents | This article surveys recent progress in the Bradley-Terry (BT) model and its extensions. We focus on the statistical and computational aspects, with emphasis on the regime in which both the number of objects and the volume of comparisons tend to infinity, a setting relevant to large-scale applications. The main topics include asymptotic theory for statistical estimation and inference, along with the associated algorithms. We also discuss applications of these models, including recent work on preference alignment in machine learning. Finally, we discuss several key challenges and outline directions for future research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_14727 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Recent advances in the Bradley--Terry Model: theory, algorithms, and applications Fang, Shuxing Han, Ruijian Luo, Yuanhang Xu, Yiming Methodology This article surveys recent progress in the Bradley-Terry (BT) model and its extensions. We focus on the statistical and computational aspects, with emphasis on the regime in which both the number of objects and the volume of comparisons tend to infinity, a setting relevant to large-scale applications. The main topics include asymptotic theory for statistical estimation and inference, along with the associated algorithms. We also discuss applications of these models, including recent work on preference alignment in machine learning. Finally, we discuss several key challenges and outline directions for future research. |
| title | Recent advances in the Bradley--Terry Model: theory, algorithms, and applications |
| topic | Methodology |
| url | https://arxiv.org/abs/2601.14727 |