Amortised Inference through One-Step Implicit Sampling
Efficient sampling from Bayesian posterior distributions in high dimensions remains a hard problem, despite advances in theory and algorithms. Multiple steps are usually required to draw unbiased samples, for example, iterations of an MCMC algorithm or integration steps in a diffusion-based amortised sampler. However,...
Vincent Pauline, Kirill Tamogashev, Arran Carter et al.
· 0 citations