Approximating non-Gaussian Bayesian partitions with normalising flows: statistics, inference and application to cosmology
Year: 2025
Type: article
Abstract: Subject of this paper is the simplification of Markov chain Monte Carlo sampling as used in Bayesian statistical inference by means of normalising flows, a machine learning method which is able to construct an invertible and differentiable transformation between Gaussian and non-Gaussian random distributions. We use normalising flows to compute Bayesian partition functions for non-Gaussian distributions and show how normalising flows can be employed in finding analytical expressions for posterior distributions beyond the Gaussian limit. Flows offer advantages for the numerical evaluation of the partition function itself, as well as for cumulants and for the information entropy. We demonstrate how normalising flows in conjunction with Bayes partitions can be used in inference problems in cosmology and apply them to the posterior distribution for the matter density Ω m and a dark energy equation of state parameter w 0 on the basis of supernova data.
Source: The Open Journal of Astrophysics
DOI: 10.33232/001c.137057
Authors: Tobias Röspel, Adrian Schlosser, Björn Malte Schäfer
Institution: ORCID
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4409705703