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December 2010 Galaxy formation: a Bayesian uncertainty analysis
Richard G. Bower, Michael Goldstein, Ian Vernon
Bayesian Anal. 5(4): 619-669 (December 2010). DOI: 10.1214/10-BA524


In many scientific disciplines complex computer models are used to understand the behaviour of large scale physical systems. An uncertainty analysis of such a computer model known as Galform is presented. Galform models the creation and evolution of approximately one million galaxies from the beginning of the Universe until the current day, and is regarded as a state-of-the-art model within the cosmology community. It requires the specification of many input parameters in order to run the simulation, takes significant time to run, and provides various outputs that can be compared with real world data. A Bayes Linear approach is presented in order to identify the subset of the input space that could give rise to acceptable matches between model output and measured data. This approach takes account of the major sources of uncertainty in a consistent and unified manner, including input parameter uncertainty, function uncertainty, observational error, forcing function uncertainty and structural uncertainty. The approach is known as History Matching, and involves the use of an iterative succession of emulators (stochastic belief specifications detailing beliefs about the Galform function), which are used to cut down the input parameter space. The analysis was successful in producing a large collection of model evaluations that exhibit good fits to the observed data.


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Richard G. Bower. Michael Goldstein. Ian Vernon. "Galaxy formation: a Bayesian uncertainty analysis." Bayesian Anal. 5 (4) 619 - 669, December 2010.


Published: December 2010
First available in Project Euclid: 19 June 2012

zbMATH: 1330.85005
MathSciNet: MR2740148
Digital Object Identifier: 10.1214/10-BA524

Keywords: Bayes linear analysis , Computer models , galaxy formation , galform , history matching , model discrepancy , uncertainty analysis

Rights: Copyright © 2010 International Society for Bayesian Analysis


Vol.5 • No. 4 • December 2010
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