Semi-Amortized Bayesian Inference with Reusable Local Posteriors
arXiv:2610.18945v1 Announce Type: new Abstract: Amortized Bayesian inference uses simulations to perform Bayesian estimation when likelihoods are expensive or analytically unavailable. However, learning the full joint posterior is challenging in high-dimensional models with complex dependencies. We…
Read the full story at arXiv stat.ML ↗
Timeline · 1 report
- 2026-10-15 04:00 · arXiv stat.ML
Semi-Amortized Bayesian Inference with Reusable Local Posteriors