Meta-analysis of Bayesian analyses
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2019
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| _version_ | 1866916435574390784 |
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| author | Blomstedt, Paul Mesquita, Diego Rivasplata, Omar Lintusaari, Jarno Sivula, Tuomas Corander, Jukka Kaski, Samuel |
| author_facet | Blomstedt, Paul Mesquita, Diego Rivasplata, Omar Lintusaari, Jarno Sivula, Tuomas Corander, Jukka Kaski, Samuel |
| contents | Meta-analysis aims to generalize results from multiple related statistical analyses through a combined analysis. While the natural outcome of a Bayesian study is a posterior distribution, traditional Bayesian meta-analyses proceed by combining summary statistics (i.e., point-valued estimates) computed from data. In this paper, we develop a framework for combining posterior distributions from multiple related Bayesian studies into a meta-analysis. Importantly, the method is capable of reusing pre-computed posteriors from computationally costly analyses, without needing the implementation details from each study. Besides providing a consensus across studies, the method enables updating the local posteriors post-hoc and therefore refining them by sharing statistical strength between the studies, without rerunning the original analyses. We illustrate the wide applicability of the framework by combining results from likelihood-free Bayesian analyses, which would be difficult to carry out using standard methodology. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1904_04484 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | Meta-analysis of Bayesian analyses Blomstedt, Paul Mesquita, Diego Rivasplata, Omar Lintusaari, Jarno Sivula, Tuomas Corander, Jukka Kaski, Samuel Methodology Meta-analysis aims to generalize results from multiple related statistical analyses through a combined analysis. While the natural outcome of a Bayesian study is a posterior distribution, traditional Bayesian meta-analyses proceed by combining summary statistics (i.e., point-valued estimates) computed from data. In this paper, we develop a framework for combining posterior distributions from multiple related Bayesian studies into a meta-analysis. Importantly, the method is capable of reusing pre-computed posteriors from computationally costly analyses, without needing the implementation details from each study. Besides providing a consensus across studies, the method enables updating the local posteriors post-hoc and therefore refining them by sharing statistical strength between the studies, without rerunning the original analyses. We illustrate the wide applicability of the framework by combining results from likelihood-free Bayesian analyses, which would be difficult to carry out using standard methodology. |
| title | Meta-analysis of Bayesian analyses |
| topic | Methodology |
| url | https://arxiv.org/abs/1904.04484 |