Statistical Science

Galaxy Formation: Bayesian History Matching for the Observable Universe

Ian Vernon, Michael Goldstein, and Richard Bower

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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.

Article information

Source
Statist. Sci., Volume 29, Number 1 (2014), 81-90.

Dates
First available in Project Euclid: 9 May 2014

Permanent link to this document
https://projecteuclid.org/euclid.ss/1399645731

Digital Object Identifier
doi:10.1214/12-STS412

Mathematical Reviews number (MathSciNet)
MR3201849

Zentralblatt MATH identifier
1332.85007

Keywords
Computer models Bayesian statistics history matching Bayes linear emulation galaxy formation

Citation

Vernon, Ian; Goldstein, Michael; Bower, Richard. Galaxy Formation: Bayesian History Matching for the Observable Universe. Statist. Sci. 29 (2014), no. 1, 81--90. doi:10.1214/12-STS412. https://projecteuclid.org/euclid.ss/1399645731


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