floZ: Evidence estimation from posterior samples with normalizing flows

arXiv:2404.12294v1 Announce Type: cross
Abstract: We propose a novel method (floZ), based on normalizing flows, for estimating the Bayesian evidence (and its numerical uncertainty) from a set of samples drawn from the unnormalized posterior distribution. We validate it on distributions whose evidence is known analytically, up to 15 parameter space dimensions, and compare with two state-of-the-art techniques for estimating the evidence: nested sampling (which computes the evidence as its main target) and a k-nearest-neighbors technique that produces evidence estimates from …

abstract arxiv astro-ph.co bayesian cs.lg dimensions distribution evidence gr-qc novel numerical posterior samples set space stat.ml type uncertainty