Bayesian evidence estimation from posterior samples with normalizing flows

Rahul Srinivasan, Marco Crisostomi, Roberto Trotta +2

We propose a novel method ($floZ$), based on normalizing flows, to estimate the Bayesian evidence (and its numerical uncertainty) from a pre-existing set of samples drawn from the…