Marginal Post Processing of Bayesian Inference Products with Normalizing Flows and Kernel Density Estimators

TL;DR

This paper introduces a method using normalizing flows and kernel density estimators to efficiently learn and utilize marginal posterior densities in Bayesian cosmology, enabling advanced analysis and emulation of complex models.

Contribution

The paper presents a novel approach combining masked autoregressive flows and kernel density estimators for marginal posterior learning, with practical applications demonstrated in cosmology.

Findings

Effective marginal posterior learning with normalizing flows.

Applications include likelihood emulation and Bayesian model analysis.

Code is made publicly available as 'margarine'.

Abstract

Bayesian analysis has become an indispensable tool across many different cosmological fields including the study of gravitational waves, the Cosmic Microwave Background and the 21-cm signal from the Cosmic Dawn among other phenomena. The method provides a way to fit complex models to data describing key cosmological and astrophysical signals and a whole host of contaminating signals and instrumental effects modelled with `nuisance parameters'. In this paper, we summarise a method that uses Masked Autoregressive Flows and Kernel Density Estimators to learn marginal posterior densities corresponding to core science parameters. We find that the marginal or 'nuisance-free' posteriors and the associated likelihoods have an abundance of applications including; the calculation of previously intractable marginal Kullback-Leibler divergences and marginal Bayesian Model Dimensionalities, likelihood emulation and prior emulation. We demonstrate each application using toy examples, examples from the field of 21-cm cosmology and samples from the Dark Energy Survey. We discuss how marginal summary statistics like the Kullback-Leibler divergences and Bayesian Model Dimensionalities can be used to examine the constraining power of different experiments and how we can perform efficient joint analysis by taking advantage of marginal prior and likelihood emulators. We package our multipurpose code up in the pip-installable code margarine for use in the wider scientific community.