Simulation-based stacking
Fuente:
arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866913248633159680 |
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| author | Yao, Yuling Blancard, Bruno Régaldo-Saint Domke, Justin |
| author_facet | Yao, Yuling Blancard, Bruno Régaldo-Saint Domke, Justin |
| contents | Simulation-based inference has been popular for amortized Bayesian computation. It is typical to have more than one posterior approximation, from different inference algorithms, different architectures, or simply the randomness of initialization and stochastic gradients. With a consistency guarantee, we present a general posterior stacking framework to make use of all available approximations. Our stacking method is able to combine densities, simulation draws, confidence intervals, and moments, and address the overall precision, calibration, coverage, and bias of the posterior approximation at the same time. We illustrate our method on several benchmark simulations and a challenging cosmological inference task. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_17009 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Simulation-based stacking Yao, Yuling Blancard, Bruno Régaldo-Saint Domke, Justin Methodology Machine Learning Computation Simulation-based inference has been popular for amortized Bayesian computation. It is typical to have more than one posterior approximation, from different inference algorithms, different architectures, or simply the randomness of initialization and stochastic gradients. With a consistency guarantee, we present a general posterior stacking framework to make use of all available approximations. Our stacking method is able to combine densities, simulation draws, confidence intervals, and moments, and address the overall precision, calibration, coverage, and bias of the posterior approximation at the same time. We illustrate our method on several benchmark simulations and a challenging cosmological inference task. |
| title | Simulation-based stacking |
| topic | Methodology Machine Learning Computation |
| url | https://arxiv.org/abs/2310.17009 |