Galaxy Formation: a Bayesian Uncertainty Analysis

作     者：Vernon, Ian Goldstein, Michael Bower, Richard G. 

作者机构：Univ Durham Sci Labs Dept Math Sci Durham DH1 3LE England Univ Durham Sci Labs Dept Phys Durham DH1 3LE England 

出 版 物：《BAYESIAN ANALYSIS》 (Bayesian Anal.)

年 卷 期：2010年第5卷第4期

页      面：619-669页

核心收录：

学科分类：02[经济学] 0202[经济学-应用经济学] 020208[经济学-统计学] 07[理学] 0714[理学-统计学（可授理学、经济学学位）] 070103[理学-概率论与数理统计] 0701[理学-数学] 

基　　金：EPSRC Durham-University EPSRC [EP/E00931X/1, EP/D048893/1] Funding Source: UKRI 

主　　题：computer models uncertainty analysis model discrepancy history matching Bayes linear analysis galaxy formation galform 

摘      要：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 an devolution of approximately one million galaxies from the beginning of the Universe until the current day, an disregarded 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 inproducing a large collection of model evaluations that exhibit good fits to the observed data.