标题： 通过神经密度估计在无似然推断中估计边缘似然性 显示英文标题

标题： Estimating Marginal Likelihoods in Likelihood-Free Inference via Neural Density Estimation

摘要： 边缘似然，或称证据，在贝叶斯模型选择中起着核心作用，但在无似然设置中仍然以计算困难而著称。 尽管基于仿真的推断（SBI）技术，如序列神经似然估计（SNLE），提供了使用神经密度估计器近似后验的强大工具，但它们通常不提供证据的估计。 在2025年BayesComp会议上发表的技术报告中，我们提出了一种简单且通用的方法，利用SNLE的输出来估计边缘似然。 显示英文摘要

摘要： 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.