arXiv Machine Learning October 19, 2026 By Kai Lehman, Jameson Dong, Carolina Cuesta-Lazaro, Niall Jeffrey, Benjamin D. Wandelt, Francisco Villaescusa-Navarro, Sven Krippendorf, Jochen Weller

ENFR: Evidence Networks with Flow-Matched References for Amortized Simulation-Based Model Inference

Read the original on arXiv Machine Learning →

The paper introduces ENFR, a method for estimating the absolute evidence ζ of a data‑generating model in Bayesian inference. It learns Bayes factors relative to a flow‑matched reference and then recovers the absolute evidence, improving over existing flow‑based baselines across multiple applications. The authors also provide a joint architecture for estimating evidences of multiple models and demonstrate its performance and scalability on diverse scientific datasets.

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