Estimating Marginal Likelihoods in Likelihood-Free Inference via Neural Density Estimation
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866911051545575424 |
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| author | Bastide, Paul Estoup, Arnaud Marin, Jean-Michel Stoehr, Julien |
| author_facet | Bastide, Paul Estoup, Arnaud Marin, Jean-Michel Stoehr, Julien |
| contents | The marginal likelihood, or evidence, plays a central role in Bayesian model selection, yet remains notoriously challenging to compute in likelihood-free settings. While Simulation-Based Inference (SBI) techniques such as Sequential Neural Likelihood Estimation (SNLE) offer powerful tools to approximate posteriors using neural density estimators, they typically do not provide estimates of the evidence. In this technical report presented at BayesComp 2025, we present a simple and general methodology to estimate the marginal likelihood using the output of SNLE. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_08734 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Estimating Marginal Likelihoods in Likelihood-Free Inference via Neural Density Estimation Bastide, Paul Estoup, Arnaud Marin, Jean-Michel Stoehr, Julien Computation The marginal likelihood, or evidence, plays a central role in Bayesian model selection, yet remains notoriously challenging to compute in likelihood-free settings. While Simulation-Based Inference (SBI) techniques such as Sequential Neural Likelihood Estimation (SNLE) offer powerful tools to approximate posteriors using neural density estimators, they typically do not provide estimates of the evidence. In this technical report presented at BayesComp 2025, we present a simple and general methodology to estimate the marginal likelihood using the output of SNLE. |
| title | Estimating Marginal Likelihoods in Likelihood-Free Inference via Neural Density Estimation |
| topic | Computation |
| url | https://arxiv.org/abs/2507.08734 |