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

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.

Gaussian Processes and Bayesian Inference Generative Adversarial Networks and Image Synthesis Markov Chains and Monte Carlo Methods

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