Hierarchical inference of evidence using posterior samples
Methodology
2024-05-14 v1
Abstract
The Bayesian evidence, crucial ingredient for model selection, is arguably the most important quantity in Bayesian data analysis: at the same time, however, it is also one of the most difficult to compute. In this paper we present a hierarchical method that leverages on a multivariate normalised approximant for the posterior probability density to infer the evidence for a model in a hierarchical fashion using a set of posterior samples drawn using an arbitrary sampling scheme.
Cite
@article{arxiv.2405.07504,
title = {Hierarchical inference of evidence using posterior samples},
author = {Stefano Rinaldi and Gabriele Demasi and Walter Del Pozzo and Otto A. Hannuksela},
journal= {arXiv preprint arXiv:2405.07504},
year = {2024}
}
Comments
18 pages, 7 figures, 1 table. Comments welcome