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February 2014 Galaxy Formation: Bayesian History Matching for the Observable Universe
Ian Vernon, Michael Goldstein, Richard Bower
Statist. Sci. 29(1): 81-90 (February 2014). DOI: 10.1214/12-STS412

Abstract

Cosmologists at the Institute of Computational Cosmology, Durham University, have developed a state of the art model of galaxy formation known as Galform, intended to contribute to our understanding of the formation, growth and subsequent evolution of galaxies in the presence of dark matter. Galform requires the specification of many input parameters and takes a significant time to complete one simulation, making comparison between the model’s output and real observations of the Universe extremely challenging. This paper concerns the analysis of this problem using Bayesian emulation within an iterative history matching strategy, and represents the most detailed uncertainty analysis of a galaxy formation simulation yet performed.

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Ian Vernon. Michael Goldstein. Richard Bower. "Galaxy Formation: Bayesian History Matching for the Observable Universe." Statist. Sci. 29 (1) 81 - 90, February 2014. https://doi.org/10.1214/12-STS412

Information

Published: February 2014
First available in Project Euclid: 9 May 2014

zbMATH: 1332.85007
MathSciNet: MR3201849
Digital Object Identifier: 10.1214/12-STS412

Rights: Copyright © 2014 Institute of Mathematical Statistics

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Vol.29 • No. 1 • February 2014
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