The authors propose a new method called floZ, which uses normalizing flows to estimate the Bayesian evidence and its numerical uncertainty from a set of posterior samples.
The key highlights are:
Normalizing flows are used to map the complex target posterior distribution to a simpler base distribution, enabling the computation of the evidence as the ratio of the unnormalized posterior to the learned flow distribution.
The loss function for training the normalizing flow is designed to not only learn the posterior distribution, but also minimize the variance of the evidence estimates across the samples and match the mean evidence estimate to the true value.
The method is validated on distributions with known analytical evidence, up to 15 parameter dimensions, and compared to nested sampling and a k-nearest neighbors technique.
floZ demonstrates superior performance, especially for complex posterior distributions and higher dimensions, where it is more robust to sharp features in the posterior.
The method has wide applicability, as it can estimate the evidence from any method that provides samples from the unnormalized posterior, such as variational inference or MCMC.
To Another Language
from source content
arxiv.org
Key Insights Distilled From
by Rahul Sriniv... at arxiv.org 04-19-2024
https://arxiv.org/pdf/2404.12294.pdfDeeper Inquiries