ENFR enables fast, amortized estimation of absolute model evidence by learning Bayes factors against a flow-matched reference—solving a key bottleneck in model validation for simulation-based inference without requiring likelihood evaluations.
This paper presents ENFR, a method for computing the evidence (marginal likelihood) of statistical models, which is crucial for model comparison and goodness-of-fit testing.