Bayes linear analysis for Bayesian optimal experimental design

Jones, M. and Goldstein, M. and Jonathan, P. and Randell, D. (2016) Bayes linear analysis for Bayesian optimal experimental design. Journal of Statistical Planning and Inference, 171. pp. 115-129. ISSN 0378-3758

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In many areas of science, models are used to describe attributes of complex systems. These models are generally themselves highly complex functions of their inputs, and can be computationally expensive to evaluate. Often, these models have parameters which must be estimated using data from the real system. In this paper, we address the problem of using prior information supplied by the model, in conjunction with prior beliefs about its parameters, to design the collection of data such that it is optimal for decisions which must be made using posterior beliefs about the model parameters. Optimal design calculations do not generally have a closed form solution, so we propose a Bayes linear analysis to find an approximately optimal design. We motivate the approach by considering optimal specification of measurement locations for remote sensing of airborne species. © 2015 Elsevier B.V..

Item Type:
Journal Article
Journal or Publication Title:
Journal of Statistical Planning and Inference
Uncontrolled Keywords:
?? bayes linear analysiscalibration problememulationinverse problemoptimal experimental designprobabilistic numericsapplied mathematicsstatistics and probabilitystatistics, probability and uncertainty ??
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Deposited On:
22 Apr 2019 17:00
Last Modified:
15 Jul 2024 19:19