Fast, accurate and lightweight sequential simulation-based inference using Gaussian locally linear mappings

arXiv:2403.07454v1 Announce Type: cross
Abstract: Bayesian inference for complex models with an intractable likelihood can be tackled using algorithms performing many calls to computer simulators. These approaches are collectively known as "simulation-based inference" (SBI). Recent SBI methods have made use of neural networks (NN) to provide approximate, yet expressive constructs for the unavailable likelihood function and the posterior distribution. However, they do not generally achieve an optimal trade-off between accuracy and computational demand. In this work, we propose an alternative …

abstract algorithms arxiv bayesian bayesian inference computer cs.lg inference likelihood linear networks neural networks simulation stat.ml type