Bayesian Model Averaging in Astrophysics: A Review
Instrumentation and Methods for Astrophysics
2013-02-08 v1 Cosmology and Nongalactic Astrophysics
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.
Keywords
Cite
@article{arxiv.1302.1721,
title = {Bayesian Model Averaging in Astrophysics: A Review},
author = {David Parkinson and Andrew R. Liddle},
journal= {arXiv preprint arXiv:1302.1721},
year = {2013}
}
Comments
16 pages, 5 figures. Invited review article for special issue on Astrostatistics