Hierarchical inference of evidence using posterior samples
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929341313581056 |
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| author | Rinaldi, Stefano Demasi, Gabriele Del Pozzo, Walter Hannuksela, Otto A. |
| author_facet | Rinaldi, Stefano Demasi, Gabriele Del Pozzo, Walter Hannuksela, Otto A. |
| contents | The Bayesian evidence, crucial ingredient for model selection, is arguably the most important quantity in Bayesian data analysis: at the same time, however, it is also one of the most difficult to compute. In this paper we present a hierarchical method that leverages on a multivariate normalised approximant for the posterior probability density to infer the evidence for a model in a hierarchical fashion using a set of posterior samples drawn using an arbitrary sampling scheme. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_07504 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Hierarchical inference of evidence using posterior samples Rinaldi, Stefano Demasi, Gabriele Del Pozzo, Walter Hannuksela, Otto A. Methodology 62F15, 62C10 The Bayesian evidence, crucial ingredient for model selection, is arguably the most important quantity in Bayesian data analysis: at the same time, however, it is also one of the most difficult to compute. In this paper we present a hierarchical method that leverages on a multivariate normalised approximant for the posterior probability density to infer the evidence for a model in a hierarchical fashion using a set of posterior samples drawn using an arbitrary sampling scheme. |
| title | Hierarchical inference of evidence using posterior samples |
| topic | Methodology 62F15, 62C10 |
| url | https://arxiv.org/abs/2405.07504 |