ENFR: Evidence Networks with Flow-Matched References for Amortized Simulation-Based Model Inference
Presents a technical advancement in Bayesian inference methodology for researchers working with simulation-based models
AI Summary
Researchers introduce ENFR, a novel method using flow-matched references for amortized evidence estimation in simulation-based Bayesian model .
Excerpt
The evidence, $\mathcal{Z} = p(x | \mathcal{M})$, of a data-generating model $\mathcal{M}$ is a powerful tool in Bayesian frameworks: it enables model inference, model misspecification tests, and goodness-of-fit assessment as an alternative to standard discrepancy statistics, e.g. $χ^2$. While versatile, the evidence is notoriously difficult to compute, even when the likelihood is available. In both likelihood-based and simulation-based inference, estimating $\mathcal{Z}$ provides an immediate s