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Bayes linear analysis for Bayesian optimal experimental design

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Bayes linear analysis for Bayesian optimal experimental design. / Jones, M.; Goldstein, M.; Jonathan, P. et al.
In: Journal of Statistical Planning and Inference, Vol. 171, 2016, p. 115-129.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Jones, M, Goldstein, M, Jonathan, P & Randell, D 2016, 'Bayes linear analysis for Bayesian optimal experimental design', Journal of Statistical Planning and Inference, vol. 171, pp. 115-129. https://doi.org/10.1016/j.jspi.2015.10.011

APA

Jones, M., Goldstein, M., Jonathan, P., & Randell, D. (2016). Bayes linear analysis for Bayesian optimal experimental design. Journal of Statistical Planning and Inference, 171, 115-129. https://doi.org/10.1016/j.jspi.2015.10.011

Vancouver

Jones M, Goldstein M, Jonathan P, Randell D. Bayes linear analysis for Bayesian optimal experimental design. Journal of Statistical Planning and Inference. 2016;171:115-129. doi: 10.1016/j.jspi.2015.10.011

Author

Jones, M. ; Goldstein, M. ; Jonathan, P. et al. / Bayes linear analysis for Bayesian optimal experimental design. In: Journal of Statistical Planning and Inference. 2016 ; Vol. 171. pp. 115-129.

Bibtex

@article{d941ed9554dd42a1b5137cc8f720c2e5,
title = "Bayes linear analysis for Bayesian optimal experimental design",
abstract = "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. {\textcopyright} 2015 Elsevier B.V..",
keywords = "Bayes linear analysis, Calibration problem, Emulation, Inverse problem, Optimal experimental design, Probabilistic numerics",
author = "M. Jones and M. Goldstein and P. Jonathan and D. Randell",
year = "2016",
doi = "10.1016/j.jspi.2015.10.011",
language = "English",
volume = "171",
pages = "115--129",
journal = "Journal of Statistical Planning and Inference",
issn = "0378-3758",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Bayes linear analysis for Bayesian optimal experimental design

AU - Jones, M.

AU - Goldstein, M.

AU - Jonathan, P.

AU - Randell, D.

PY - 2016

Y1 - 2016

N2 - 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..

AB - 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..

KW - Bayes linear analysis

KW - Calibration problem

KW - Emulation

KW - Inverse problem

KW - Optimal experimental design

KW - Probabilistic numerics

U2 - 10.1016/j.jspi.2015.10.011

DO - 10.1016/j.jspi.2015.10.011

M3 - Journal article

VL - 171

SP - 115

EP - 129

JO - Journal of Statistical Planning and Inference

JF - Journal of Statistical Planning and Inference

SN - 0378-3758

ER -