Neural Methods for Amortised Parameter Inference

arXiv:2404.12484v1 Announce Type: cross
Abstract: Simulation-based methods for making statistical inference have evolved dramatically over the past 50 years, keeping pace with technological advancements. The field is undergoing a new revolution as it embraces the representational capacity of neural networks, optimisation libraries, and graphics processing units for learning complex mappings between data and inferential targets. The resulting tools are amortised, in the sense that they allow inference to be made quickly through fast feedforward operations. In this article we review …

abstract arxiv capacity cs.lg data graphics graphics processing units inference libraries making networks neural networks optimisation processing simulation stat.co statistical stat.ml type units