A Bayesian approach for parameter estimation and prediction using a computationally intensive model

Higdon, Dave; McDonnell, Jordan D.; Schunck, Nicolas; Sarich, Jason; Wild, Stefan M.

doi:10.1088/0954-3899/42/3/034009

Title: A Bayesian approach for parameter estimation and prediction using a computationally intensive model

Journal Article · Thu Feb 05 00:00:00 EST 2015 · Journal of Physics. G, Nuclear and Particle Physics

DOI:https://doi.org/10.1088/0954-3899/42/3/034009· OSTI ID:1378523

Higdon, Dave ^[1]; McDonnell, Jordan D. ^[2]; Schunck, Nicolas ^[2]; Sarich, Jason ^[3]; Wild, Stefan M. ^[3]

Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). Physics Division
Argonne National Lab. (ANL), Argonne, IL (United States). Mathematics and Computer Science Division

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF); Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States); Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)

Grant/Contract Number:: AC52-07NA27344; AC52-06NA25396

OSTI ID:: 1378523

Alternate ID(s):: OSTI ID: 1407869

Report Number(s):: LLNL-JRNL-737147; LA-UR-14-26925

Journal Information:: Journal of Physics. G, Nuclear and Particle Physics, Vol. 42, Issue 3; ISSN 0954-3899

Publisher:: IOP PublishingCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 32 works

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References (30)

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Cited By (9)

Bayesian estimation of the specific shear and bulk viscosity of quark–gluon plasma Bernhard, Jonah E.; Moreland, J. Scott; Bass, Steffen A. Nature Physics, Vol. 15, Issue 11 https://doi.org/10.1038/s41567-019-0611-8	journal	August 2019
Error analysis in nuclear density functional theory Schunck, Nicolas; McDonnell, Jordan D.; Sarich, Jason Journal of Physics G: Nuclear and Particle Physics, Vol. 42, Issue 3 https://doi.org/10.1088/0954-3899/42/3/034024	journal	February 2015
Bayesian parameter estimation for effective field theories Wesolowski, S.; Klco, N.; Furnstahl, R. J. Journal of Physics G: Nuclear and Particle Physics, Vol. 43, Issue 7 https://doi.org/10.1088/0954-3899/43/7/074001	journal	May 2016
Control functionals for Monte Carlo integration Oates, Chris J.; Girolami, Mark; Chopin, Nicolas Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 79, Issue 3 https://doi.org/10.1111/rssb.12185	journal	May 2016
Error Analysis in Nuclear Density Functional Theory Schunck, Nicolas; McDonnell, Jordan D.; Sarich, Jason arXiv https://doi.org/10.48550/arxiv.1406.4383	text	January 2014
Control functionals for Monte Carlo integration Oates, Chris J.; Girolami, Mark; Chopin, Nicolas arXiv https://doi.org/10.48550/arxiv.1410.2392	preprint	January 2014
Uncertainty Quantification for Nuclear Density Functional Theory and Information Content of New Measurements McDonnell, J. D.; Schunck, N.; Higdon, D. arXiv https://doi.org/10.48550/arxiv.1501.03572	text	January 2015
Applying Bayesian parameter estimation to relativistic heavy-ion collisions: simultaneous characterization of the initial state and quark-gluon plasma medium Bernhard, Jonah E.; Moreland, J. Scott; Bass, Steffen A. arXiv https://doi.org/10.48550/arxiv.1605.03954	text	January 2016
Constraints on rapidity-dependent initial conditions from charged particle pseudorapidity densities and two-particle correlations Ke, Weiyao; Moreland, J. Scott; Bernhard, Jonah E. arXiv https://doi.org/10.48550/arxiv.1610.08490	text	January 2016

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