Bayesian Model Averaging in Astrophysics: A Review

TL;DR

This review paper discusses Bayesian Model Averaging techniques and their applications in astrophysics, covering statistical methods and various cosmological and astronomical measurements.

Contribution

It provides a comprehensive overview of Bayesian Model Averaging methods and their diverse applications in astrophysics research.

Findings

Bayesian Model Averaging enhances parameter estimation accuracy.

Various computational methods like MCMC and nested sampling are used.

Applications include dark energy, primordial spectrum, and star classification.

Abstract

We review the use of Bayesian Model Averaging in astrophysics. We first introduce the statistical basis of Bayesian Model Selection and Model Averaging. We discuss methods to calculate the model-averaged posteriors, including Markov Chain Monte Carlo (MCMC), nested sampling, Population Monte Carlo, and Reversible Jump MCMC. We then review some applications of Bayesian Model Averaging in astrophysics, including measurements of the dark energy and primordial power spectrum parameters in cosmology, cluster weak lensing and Sunyaev-Zel'dovich effect data, estimating distances to Cepheids, and classifying variable stars.