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.

翻译：边际似然，或称证据，在贝叶斯模型选择中起着核心作用，但在免似然设置中其计算仍然极具挑战性。虽然基于模拟的推断（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来近似后验分布的强大工具，但它们通常不提供证据的估计值。在这篇提交至BayesComp 2025的技术报告中，我们提出了一种利用SNLE的输出结果来估计边际似然的简单且通用的方法。