Being Bayesian in the 2020s: opportunities and challenges in the practice of modern applied Bayesian statistics
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2022
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| _version_ | 1866908748500434944 |
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| author | Bon, Joshua J. Bretherton, Adam Buchhorn, Katie Cramb, Susanna Drovandi, Christopher Hassan, Conor Jenner, Adrianne L. Mayfield, Helen J. McGree, James M. Mengersen, Kerrie Price, Aiden Salomone, Robert Santos-Fernandez, Edgar Vercelloni, Julie Wang, Xiaoyu |
| author_facet | Bon, Joshua J. Bretherton, Adam Buchhorn, Katie Cramb, Susanna Drovandi, Christopher Hassan, Conor Jenner, Adrianne L. Mayfield, Helen J. McGree, James M. Mengersen, Kerrie Price, Aiden Salomone, Robert Santos-Fernandez, Edgar Vercelloni, Julie Wang, Xiaoyu |
| contents | Building on a strong foundation of philosophy, theory, methods and computation over the past three decades, Bayesian approaches are now an integral part of the toolkit for most statisticians and data scientists. Whether they are dedicated Bayesians or opportunistic users, applied professionals can now reap many of the benefits afforded by the Bayesian paradigm. In this paper, we touch on six modern opportunities and challenges in applied Bayesian statistics: intelligent data collection, new data sources, federated analysis, inference for implicit models, model transfer and purposeful software products. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2211_10029 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Being Bayesian in the 2020s: opportunities and challenges in the practice of modern applied Bayesian statistics Bon, Joshua J. Bretherton, Adam Buchhorn, Katie Cramb, Susanna Drovandi, Christopher Hassan, Conor Jenner, Adrianne L. Mayfield, Helen J. McGree, James M. Mengersen, Kerrie Price, Aiden Salomone, Robert Santos-Fernandez, Edgar Vercelloni, Julie Wang, Xiaoyu Applications Building on a strong foundation of philosophy, theory, methods and computation over the past three decades, Bayesian approaches are now an integral part of the toolkit for most statisticians and data scientists. Whether they are dedicated Bayesians or opportunistic users, applied professionals can now reap many of the benefits afforded by the Bayesian paradigm. In this paper, we touch on six modern opportunities and challenges in applied Bayesian statistics: intelligent data collection, new data sources, federated analysis, inference for implicit models, model transfer and purposeful software products. |
| title | Being Bayesian in the 2020s: opportunities and challenges in the practice of modern applied Bayesian statistics |
| topic | Applications |
| url | https://arxiv.org/abs/2211.10029 |