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arXiv · 2405.07504

Hierarchical inference of evidence using posterior samples

Stefano Rinaldi·Gabriele Demasi·Walter Del Pozzo·Otto A. Hannuksela
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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.

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Stefano Rinaldi, Gabriele Demasi, Walter Del Pozzo, Otto A. Hannuksela. 2024-05-13. Hierarchical inference of evidence using posterior samples. https://arxiv.org/abs/2405.07504

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