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Galaxy Formation: a Bayesian Uncertainty Analysis

Vernon, Ian; Goldstein, Michael; Bower, Richard G.

Galaxy Formation: a Bayesian Uncertainty Analysis Thumbnail


Richard G. Bower


In many scientific disciplines complex computer models are used to understand the behaviour of large scale physical systems. An uncertainty anal- ysis of such a computer model known as Galform is presented. Galform models the creation and evolution of approximately one million galaxies from the begin- ning 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 in- put 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 suc- cession 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.


Vernon, I., Goldstein, M., & Bower, R. G. (2010). Galaxy Formation: a Bayesian Uncertainty Analysis. Bayesian Analysis, 05(04), 619-670.

Journal Article Type Article
Publication Date Dec 1, 2010
Deposit Date Mar 21, 2011
Publicly Available Date Apr 6, 2011
Journal Bayesian Analysis
Print ISSN 1936-0975
Electronic ISSN 1931-6690
Publisher International Society for Bayesian Analysis (ISBA)
Peer Reviewed Peer Reviewed
Volume 05
Issue 04
Pages 619-670
Keywords Computer models, Uncertainty analysis, Model discrepancy, History matching, Bayes linear analysis, Galaxy formation, Galform.
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