Detecting and diagnosing prior and likelihood sensitivity with power-scaling
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2021
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| Soggetti: | |
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| _version_ | 1866916080785555456 |
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| author | Kallioinen, Noa Paananen, Topi Bürkner, Paul-Christian Vehtari, Aki |
| author_facet | Kallioinen, Noa Paananen, Topi Bürkner, Paul-Christian Vehtari, Aki |
| contents | Determining the sensitivity of the posterior to perturbations of the prior and likelihood is an important part of the Bayesian workflow. We introduce a practical and computationally efficient sensitivity analysis approach using importance sampling to estimate properties of posteriors resulting from power-scaling the prior or likelihood. On this basis, we suggest a diagnostic that can indicate the presence of prior-data conflict or likelihood noninformativity and discuss limitations to this power-scaling approach. The approach can be easily included in Bayesian workflows with minimal effort by the model builder and we present an implementation in our new R package priorsense. We further demonstrate the workflow on case studies of real data using models varying in complexity from simple linear models to Gaussian process models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2107_14054 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Detecting and diagnosing prior and likelihood sensitivity with power-scaling Kallioinen, Noa Paananen, Topi Bürkner, Paul-Christian Vehtari, Aki Methodology Determining the sensitivity of the posterior to perturbations of the prior and likelihood is an important part of the Bayesian workflow. We introduce a practical and computationally efficient sensitivity analysis approach using importance sampling to estimate properties of posteriors resulting from power-scaling the prior or likelihood. On this basis, we suggest a diagnostic that can indicate the presence of prior-data conflict or likelihood noninformativity and discuss limitations to this power-scaling approach. The approach can be easily included in Bayesian workflows with minimal effort by the model builder and we present an implementation in our new R package priorsense. We further demonstrate the workflow on case studies of real data using models varying in complexity from simple linear models to Gaussian process models. |
| title | Detecting and diagnosing prior and likelihood sensitivity with power-scaling |
| topic | Methodology |
| url | https://arxiv.org/abs/2107.14054 |