Diffusion posterior sampling for simulation-based inference in tall data settings

arXiv:2404.07593v1 Announce Type: cross
Abstract: Determining which parameters of a non-linear model could best describe a set of experimental data is a fundamental problem in science and it has gained much traction lately with the rise of complex large-scale simulators (a.k.a. black-box simulators). The likelihood of such models is typically intractable, which is why classical MCMC methods can not be used. Simulation-based inference (SBI) stands out in this context by only requiring a dataset of simulations to train deep generative …

abstract arxiv box cs.lg data diffusion experimental inference likelihood linear linear model non-linear parameters posterior sampling scale science set simulation stat.me stat.ml type