Fully Bayesian Forecasts with Evidence Networks
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866909208469831680 |
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| author | Gessey-Jones, T. Handley, W. J. |
| author_facet | Gessey-Jones, T. Handley, W. J. |
| contents | Sensitivity forecasts inform the design of experiments and the direction of theoretical efforts. To arrive at representative results, Bayesian forecasts should marginalize their conclusions over uncertain parameters and noise realizations rather than picking fiducial values. However, this is typically computationally infeasible with current methods for forecasts of an experiment's ability to distinguish between competing models. We thus propose a novel simulation-based methodology capable of providing expedient and rigorous Bayesian model comparison forecasts without relying on restrictive assumptions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_06942 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Fully Bayesian Forecasts with Evidence Networks Gessey-Jones, T. Handley, W. J. Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics General Relativity and Quantum Cosmology Sensitivity forecasts inform the design of experiments and the direction of theoretical efforts. To arrive at representative results, Bayesian forecasts should marginalize their conclusions over uncertain parameters and noise realizations rather than picking fiducial values. However, this is typically computationally infeasible with current methods for forecasts of an experiment's ability to distinguish between competing models. We thus propose a novel simulation-based methodology capable of providing expedient and rigorous Bayesian model comparison forecasts without relying on restrictive assumptions. |
| title | Fully Bayesian Forecasts with Evidence Networks |
| topic | Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics General Relativity and Quantum Cosmology |
| url | https://arxiv.org/abs/2309.06942 |