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

Paper recorded by Signals 4 on 2026-10-16 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-16 on arXiv · recorded by Signals 4 on 2026-10-19

Category: cs.LG · 机器学习 · first seen 2026-10-19

Abstract

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 bo

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#251 most recent of 749 cs.LG papers we have recorded · ↑ newer: Quality-of-Transmission Estimation in Quantum Networks: Compute, Simul · ↓ older: Learning without relearning physical symmetry: an SO(3)-equivariant co
Cite this page: ENFR: Evidence Networks with Flow-Matched References for Amortized Simulation-Based Model Inference: the #251 most recent of 749 cs.LG papers we have recorded (as of 2026-10-16). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/enfr-evidence-networks-with-flow-matched-references-for-amortized-simulation-bas.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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