Bayesian model averaging in astrophysics: a review

Parkinson, David and Liddle, Andrew (2013) Bayesian model averaging in astrophysics: a review. Statistical Analysis and Data Mining, 6 1: 3-14. doi:10.1002/sam.11179


Author Parkinson, David
Liddle, Andrew
Title Bayesian model averaging in astrophysics: a review
Journal name Statistical Analysis and Data Mining   Check publisher's open access policy
ISSN 1932-1864
1932-1872
Publication date 2013-01-01
Year available 2013
Sub-type Critical review of research, literature review, critical commentary
DOI 10.1002/sam.11179
Open Access Status Not yet assessed
Volume 6
Issue 1
Start page 3
End page 14
Total pages 12
Place of publication Hoboken, NJ, United States
Publisher John Wiley & Sons
Language eng
Subject 2603 Analysis
1710 Information Systems
1706 Computer Science Applications
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 (RJMCMC). 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.
Keyword Cosmology
Data analysis -methods
Methods
Statistical
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Special Issue on Astrostatistics.

Document type: Journal Article
Sub-type: Critical review of research, literature review, critical commentary
Collections: School of Mathematics and Physics
Official 2014 Collection
 
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Citation counts: TR Web of Science Citation Count  Cited 8 times in Thomson Reuters Web of Science Article | Citations
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Created: Thu, 09 May 2013, 03:26:50 EST by Dr David Parkinson on behalf of Physics