Probabilistic Inference and Learning with Stein's Method
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866911496203665408 |
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| author | Liu, Qiang Mackey, Lester Oates, Chris |
| author_facet | Liu, Qiang Mackey, Lester Oates, Chris |
| contents | This monograph provides a rigorous overview of theoretical and methodological aspects of probabilistic inference and learning with Stein's method. Recipes are provided for constructing Stein discrepancies from Stein operators and Stein sets, and properties of these discrepancies such as computability, separation, convergence detection, and convergence control are discussed. Further, the connection between Stein operators and Stein variational gradient descent is set out in detail. The main definitions and results are precisely stated, and references to all proofs are provided. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_07467 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Probabilistic Inference and Learning with Stein's Method Liu, Qiang Mackey, Lester Oates, Chris Machine Learning Probability Statistics Theory Methodology This monograph provides a rigorous overview of theoretical and methodological aspects of probabilistic inference and learning with Stein's method. Recipes are provided for constructing Stein discrepancies from Stein operators and Stein sets, and properties of these discrepancies such as computability, separation, convergence detection, and convergence control are discussed. Further, the connection between Stein operators and Stein variational gradient descent is set out in detail. The main definitions and results are precisely stated, and references to all proofs are provided. |
| title | Probabilistic Inference and Learning with Stein's Method |
| topic | Machine Learning Probability Statistics Theory Methodology |
| url | https://arxiv.org/abs/2603.07467 |