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Main Authors: Bastide, Paul, Estoup, Arnaud, Marin, Jean-Michel, Stoehr, Julien
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2507.08734
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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.
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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