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

Authors:

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

Publication Date:: Thu Feb 05 00:00:00 EST 2015

Research Org.:: 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 Org.:: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)

OSTI Identifier:: 1378523

Alternate Identifier(s):: OSTI ID: 1407869

Report Number(s):: LLNL-JRNL-737147; LA-UR-14-26925
Journal ID: ISSN 0954-3899

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

Resource Type:: Accepted Manuscript

Journal Name:: Journal of Physics. G, Nuclear and Particle Physics

Additional Journal Information:: Journal Volume: 42; Journal Issue: 3; Journal ID: ISSN 0954-3899

Publisher:: IOP Publishing

Country of Publication:: United States

Language:: English

Subject:: 73 NUCLEAR PHYSICS AND RADIATION PHYSICS; 97 MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE

Citation Formats


                    Higdon, Dave, McDonnell, Jordan D., Schunck, Nicolas, Sarich, Jason, and Wild, Stefan M. A Bayesian approach for parameter estimation and prediction using a computationally intensive model.  United States: N. p., 2015. 
Web.  doi:10.1088/0954-3899/42/3/034009.

Copy to clipboard


                    Higdon, Dave, McDonnell, Jordan D., Schunck, Nicolas, Sarich, Jason, & Wild, Stefan M. A Bayesian approach for parameter estimation and prediction using a computationally intensive model.  United States.  https://doi.org/10.1088/0954-3899/42/3/034009

Copy to clipboard


                    Higdon, Dave, McDonnell, Jordan D., Schunck, Nicolas, Sarich, Jason, and Wild, Stefan M. Thu .  
"A Bayesian approach for parameter estimation and prediction using a computationally intensive model".  United States.  https://doi.org/10.1088/0954-3899/42/3/034009.  https://www.osti.gov/servlets/purl/1378523.

Copy to clipboard


                    
@article{osti_1378523,

  title        = {A Bayesian approach for parameter estimation and prediction using a computationally intensive model},

  author       = {Higdon, Dave and McDonnell, Jordan D. and Schunck, Nicolas and Sarich, Jason and Wild, Stefan M.},

  abstractNote = {},

  doi          = {10.1088/0954-3899/42/3/034009},

  journal      = {Journal of Physics. G, Nuclear and Particle Physics},

  number       = 3,

  volume       = 42,

  place        = {United States},

  year         = {Thu Feb 05 00:00:00 EST 2015},

  month        = {Thu Feb 05 00:00:00 EST 2015}

}

Copy to clipboard

Journal Article:

Free Publicly Available Full Text

Accepted Manuscript (DOE)

Publisher's Version of Record

https://doi.org/10.1088/0954-3899/42/3/034009

Other availability

Search WorldCat to find libraries that may hold this journal

Citation Metrics:

Cited by: 32 works

Citation information provided by
Web of Science

Save / Share:

Export Metadata

Save to My Library

Works referenced in this record:

Using Statistical and Computer Models to Quantify Volcanic Hazards
journal, November 2009

Bayarri, M. J.; Berger, James O.; Calder, Eliza S.
Technometrics, Vol. 51, Issue 4
DOI: 10.1198/TECH.2009.08018

Inverse Problem Theory and Methods for Model Parameter Estimation
book, January 2005

Tarantola, Albert
DOI: 10.1137/1.9780898717921

Error analysis in nuclear density functional theory
journal, February 2015

Schunck, Nicolas; McDonnell, Jordan D.; Sarich, Jason
Journal of Physics G: Nuclear and Particle Physics, Vol. 42, Issue 3
DOI: 10.1088/0954-3899/42/3/034024

Bayesian Computation and Stochastic Systems
journal, February 1995

Besag, Julian; Green, Peter; Higdon, David
Statistical Science, Vol. 10, Issue 1
DOI: 10.1214/ss/1177010123

Computer Model Calibration Using High-Dimensional Output
journal, June 2008

Higdon, Dave; Gattiker, James; Williams, Brian
Journal of the American Statistical Association, Vol. 103, Issue 482
DOI: 10.1198/016214507000000888

The Design and Analysis of Computer Experiments
book, January 2003

Santner, Thomas J.; Williams, Brian J.; Notz, William I.
Springer Series in Statistics
DOI: 10.1007/978-1-4757-3799-8

Efficient Emulators for Multivariate Deterministic Functions
journal, December 2008

Rougier, Jonathan
Journal of Computational and Graphical Statistics, Vol. 17, Issue 4
DOI: 10.1198/106186008X384032

Bayesian calibration of computer models
journal, August 2001

Kennedy, Marc C.; O'Hagan, Anthony
Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 63, Issue 3
DOI: 10.1111/1467-9868.00294

Screening, Predicting, and Computer Experiments
journal, February 1992

Welch, William J.; Buck, Robert. J.; Sacks, Jerome
Technometrics, Vol. 34, Issue 1
DOI: 10.2307/1269548

Cosmic Calibration
journal, July 2006

Heitmann, Katrin; Higdon, David; Nakhleh, Charles
The Astrophysical Journal, Vol. 646, Issue 1
DOI: 10.1086/506448

Nuclear energy density optimization
journal, August 2010

Kortelainen, M.; Lesinski, T.; Moré, J.
Physical Review C, Vol. 82, Issue 2
DOI: 10.1103/PhysRevC.82.024313

Bayesian analysis of computer code outputs: A tutorial
journal, October 2006

O’Hagan, A.
Reliability Engineering & System Safety, Vol. 91, Issue 10-11
DOI: 10.1016/j.ress.2005.11.025

Probabilistic sensitivity analysis of complex models: a Bayesian approach
journal, August 2004

Oakley, Jeremy E.; O'Hagan, Anthony
Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 66, Issue 3
DOI: 10.1111/j.1467-9868.2004.05304.x

A Framework for Validation of Computer Models
journal, May 2007

Bayarri, Maria J.; Berger, James O.; Paulo, Rui
Technometrics, Vol. 49, Issue 2
DOI: 10.1198/004017007000000092

Combining Field Data and Computer Simulations for Calibration and Prediction
journal, January 2004

Higdon, Dave; Kennedy, Marc; Cavendish, James C.
SIAM Journal on Scientific Computing, Vol. 26, Issue 2
DOI: 10.1137/S1064827503426693

Design and Analysis of Computer Experiments
journal, November 1989

Sacks, Jerome; Welch, William J.; Mitchell, Toby J.
Statistical Science, Vol. 4, Issue 4
DOI: 10.1214/ss/1177012413

Modeling Data from Computer Experiments: An Empirical Comparison of Kriging with MARS and Projection Pursuit Regression
journal, October 2007

Ben-Ari, Einat Neumann; Steinberg, David M.
Quality Engineering, Vol. 19, Issue 4
DOI: 10.1080/08982110701580930

Equation of State Calculations by Fast Computing Machines
journal, June 1953

Metropolis, Nicholas; Rosenbluth, Arianna W.; Rosenbluth, Marshall N.
The Journal of Chemical Physics, Vol. 21, Issue 6
DOI: 10.1063/1.1699114

Orthogonal Array-Based Latin Hypercubes
journal, December 1993

Tang, Boxin
Journal of the American Statistical Association, Vol. 88, Issue 424
DOI: 10.1080/01621459.1993.10476423

Monte Carlo sampling methods using Markov chains and their applications
journal, April 1970

Hastings, W. K.
Biometrika, Vol. 57, Issue 1
DOI: 10.1093/biomet/57.1.97

Nuclear energy density optimization: Large deformations
journal, February 2012

Kortelainen, M.; McDonnell, J.; Nazarewicz, W.
Physical Review C, Vol. 85, Issue 2
DOI: 10.1103/PhysRevC.85.024304

Functional Data Analysis
book, January 2005

Ramsay, James; Everitt, Brian S.; Howell, David C.
Encyclopedia of Statistics in Behavioral Science
DOI: 10.1002/0470013192.bsa239

Orthogonal Array-Based Latin Hypercubes
journal, December 1993

Tang, Boxin
Journal of the American Statistical Association, Vol. 88, Issue 424
DOI: 10.2307/2291282

Design and analysis of computer experiments
conference, September 1998

Booker, Andrew
7th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization
DOI: 10.2514/6.1998-4757

The Design and Analysis of Computer Experiments
book, January 2018

Santner, Thomas J.; Williams, Brian J.; Notz, William I.
Springer Series in Statistics
DOI: 10.1007/978-1-4939-8847-1

Design and analysis of computer experiments
journal, December 2010

Kuhnt, Sonja; Steinberg, David M.
AStA Advances in Statistical Analysis, Vol. 94, Issue 4
DOI: 10.1007/s10182-010-0143-0

Functional Data Analysis
book, January 2005

Ramsay, J. O.; Silverman, B. W.
Springer Series in Statistics
DOI: 10.1007/b98888

First Results from the CARIBU Facility: Mass Measurements on the r-Process Path
text, January 2013

Van Schelt, J.; Lascar, D.; Savard, G.
arXiv
DOI: 10.48550/arxiv.1307.0429

Error Analysis in Nuclear Density Functional Theory
text, January 2014

Schunck, Nicolas; McDonnell, Jordan D.; Sarich, Jason
arXiv
DOI: 10.48550/arxiv.1406.4383

Cosmic Calibration
text, January 2006

Heitmann, Katrin; Higdon, David; Nakhleh, Charles
arXiv
DOI: 10.48550/arxiv.astro-ph/0606154

Works referencing / citing this record:

Bayesian estimation of the specific shear and bulk viscosity of quark–gluon plasma
journal, August 2019

Bernhard, Jonah E.; Moreland, J. Scott; Bass, Steffen A.
Nature Physics, Vol. 15, Issue 11
DOI: 10.1038/s41567-019-0611-8

Error analysis in nuclear density functional theory
journal, February 2015

Schunck, Nicolas; McDonnell, Jordan D.; Sarich, Jason
Journal of Physics G: Nuclear and Particle Physics, Vol. 42, Issue 3
DOI: 10.1088/0954-3899/42/3/034024

Bayesian parameter estimation for effective field theories
journal, May 2016

Wesolowski, S.; Klco, N.; Furnstahl, R. J.
Journal of Physics G: Nuclear and Particle Physics, Vol. 43, Issue 7
DOI: 10.1088/0954-3899/43/7/074001

Control functionals for Monte Carlo integration
journal, May 2016

Oates, Chris J.; Girolami, Mark; Chopin, Nicolas
Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 79, Issue 3
DOI: 10.1111/rssb.12185

Error Analysis in Nuclear Density Functional Theory
text, January 2014

Schunck, Nicolas; McDonnell, Jordan D.; Sarich, Jason
arXiv
DOI: 10.48550/arxiv.1406.4383

Control functionals for Monte Carlo integration
preprint, January 2014

Oates, Chris J.; Girolami, Mark; Chopin, Nicolas
arXiv
DOI: 10.48550/arxiv.1410.2392

Uncertainty Quantification for Nuclear Density Functional Theory and Information Content of New Measurements
text, January 2015

McDonnell, J. D.; Schunck, N.; Higdon, D.
arXiv
DOI: 10.48550/arxiv.1501.03572

Applying Bayesian parameter estimation to relativistic heavy-ion collisions: simultaneous characterization of the initial state and quark-gluon plasma medium
text, January 2016

Bernhard, Jonah E.; Moreland, J. Scott; Bass, Steffen A.
arXiv
DOI: 10.48550/arxiv.1605.03954

Constraints on rapidity-dependent initial conditions from charged particle pseudorapidity densities and two-particle correlations
text, January 2016

Ke, Weiyao; Moreland, J. Scott; Bernhard, Jonah E.
arXiv
DOI: 10.48550/arxiv.1610.08490

Error Analysis in Nuclear Density Functional Theory
text, January 2014

Schunck, Nicolas; McDonnell, Jordan D.; Sarich, Jason
arXiv
DOI: 10.48550/arxiv.1406.4383

Similar Records in DOE PAGES and OSTI.GOV collections:

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

Journal Article Higdon, Dave ; McDonnell, Jordan D. ; Schunck, Nicolas ; ... - Journal of Physics. G, Nuclear and Particle Physics

Bayesian methods have been successful in quantifying uncertainty in physics-based problems in parameter estimation and prediction. In these cases, physical measurements y are modeled as the best fit of a physics-based model eta(theta), where theta denotes the uncertain, best input setting. Hence the statistical model is of the form y = eta(theta) + c, where epsilon accounts for measurement, and possibly other, error sources. When nonlinearity is present in eta(center dot), the resulting posterior distribution for the unknown parameters in the Bayesian formulation is typically complex and nonstandard, requiring computationally demanding computational approaches such as Markov chain Monte Carlo (MCMC)more »« less
https://doi.org/10.1088/0954-3899/42/3/034009
A Bayesian Approach to Real-Time Dynamic Parameter Estimation Using Phasor Measurement Unit Measurement

Journal Article Xu, Yijun ; Mili, Lamine ; Chen, Xiao ; ... - IEEE Transactions on Power Systems

Here, we develop a polynomial-chaos-expansion (PCE)-based approach for decentralized dynamic parameter estimation through Bayesian inference. Using this approach, the non-Gaussian distribution of the inverted parameters is obtained. More specifically, we first represent the decentralized generator model with the PCE-based surrogate. This surrogate allows us to efficiently evaluate the time-consuming dynamic solver at parameter values through Metropolis-Hastings (M-H)-based Markov chain Monte Carlo (MCMC). Then, we propose a two-stage hybrid Markov chain Monte Carlo (MCMC) to recover a posteriori distribution of the decentralized generator model parameters. In the first stage, we use the gradient-enhanced Langevin MCMC algorithm to characterize an intermediate posteriormore »« less
https://doi.org/10.1109/tpwrs.2019.2942133

Full Text Available
A Bayesian Approach to Regional Decadal Predictability: Sparse Parameter Estimation in High-Dimensional Linear Inverse Models of High-Latitude Sea Surface Temperature Variability

Journal Article Foster, Dallas Allen Scott ; Comeau, Darin Scott ; Urban, Nathan Mark - Journal of Climate

Stochastic reduced models are an important tool in climate systems whose many spatial and temporal scales cannot be fully discretized or underlying physics may not be fully accounted for. One form of reduced model, the linear inverse model (LIM), has been widely used for regional climate predictability studies—typically focusing more on tropical or midlatitude studies. However, most LIM fitting techniques rely on point estimation techniques deriving from fluctuation–dissipation theory. In this methodological study we explore the use of Bayesian inference techniques for LIM parameter estimation of sea surface temperature (SST), to quantify the skillful decadal predictability of Bayesian LIM modelsmore »« less
https://doi.org/10.1175/jcli-d-19-0769.1

Full Text Available
A Bayesian Modeling Approach for Estimation of a Shape-Free Groundwater Age Distribution using Multiple Tracers

Journal Article Massoudieh, Arash ; Visser, Ate ; Sharifi, Soroosh ; ... - Applied Geochemistry

The mixing of groundwaters with different ages in aquifers, groundwater age is more appropriately represented by a distribution rather than a scalar number. To infer a groundwater age distribution from environmental tracers, a mathematical form is often assumed for the shape of the distribution and the parameters of the mathematical distribution are estimated using deterministic or stochastic inverse methods. We found that the prescription of the mathematical form limits the exploration of the age distribution to the shapes that can be described by the selected distribution. In this paper, the use of freeform histograms as groundwater age distributions is evaluated.more »« less
Cited by 40
https://doi.org/10.1016/j.apgeochem.2013.10.004

Full Text Available
Determining the jet transport coefficient $$\hat{q}$$ from inclusive hadron suppression measurements using Bayesian parameter estimation

Journal Article Cao, S. ; Chen, Y. ; Coleman, J. ; ... - Physical Review C

We report a new determination of ˆq, the jet transport coefficient of the Quark-Gluon Plasma. We use the JETSCAPE framework, which incorporates a novel multi-stage theoretical approach to in-medium jet evolution and Bayesian inference for parameter extraction. The calculations, based on the Matter and Lbt jet quenching models, are compared to experimental measurements of inclusive hadron suppression in Au + Au collisions at RHIC and Pb + Pb collisions at the LHC. The correlation of experimental systematic uncertainties is accounted for in the parameter extraction. The functional dependence of ˆq on jet energy or virtuality and medium temperature is basedmore »« less
https://doi.org/10.1103/physrevc.104.024905

Full Text Available

Similar Records

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

Citation Formats

Using Statistical and Computer Models to Quantify Volcanic Hazards journal, November 2009

Inverse Problem Theory and Methods for Model Parameter Estimation book, January 2005

Error analysis in nuclear density functional theory journal, February 2015

Bayesian Computation and Stochastic Systems journal, February 1995

Computer Model Calibration Using High-Dimensional Output journal, June 2008

The Design and Analysis of Computer Experiments book, January 2003

Efficient Emulators for Multivariate Deterministic Functions journal, December 2008

Bayesian calibration of computer models journal, August 2001

Screening, Predicting, and Computer Experiments journal, February 1992

Cosmic Calibration journal, July 2006

Nuclear energy density optimization journal, August 2010

Bayesian analysis of computer code outputs: A tutorial journal, October 2006

Probabilistic sensitivity analysis of complex models: a Bayesian approach journal, August 2004

A Framework for Validation of Computer Models journal, May 2007

Combining Field Data and Computer Simulations for Calibration and Prediction journal, January 2004

Design and Analysis of Computer Experiments journal, November 1989

Modeling Data from Computer Experiments: An Empirical Comparison of Kriging with MARS and Projection Pursuit Regression journal, October 2007

Equation of State Calculations by Fast Computing Machines journal, June 1953

Orthogonal Array-Based Latin Hypercubes journal, December 1993

Monte Carlo sampling methods using Markov chains and their applications journal, April 1970

Nuclear energy density optimization: Large deformations journal, February 2012

Functional Data Analysis book, January 2005

Orthogonal Array-Based Latin Hypercubes journal, December 1993

Design and analysis of computer experiments conference, September 1998

The Design and Analysis of Computer Experiments book, January 2018

Design and analysis of computer experiments journal, December 2010

Functional Data Analysis book, January 2005

First Results from the CARIBU Facility: Mass Measurements on the r-Process Path text, January 2013

Error Analysis in Nuclear Density Functional Theory text, January 2014

Cosmic Calibration text, January 2006

Bayesian estimation of the specific shear and bulk viscosity of quark–gluon plasma journal, August 2019

Error analysis in nuclear density functional theory journal, February 2015

Bayesian parameter estimation for effective field theories journal, May 2016

Control functionals for Monte Carlo integration journal, May 2016

Error Analysis in Nuclear Density Functional Theory text, January 2014

Control functionals for Monte Carlo integration preprint, January 2014

Uncertainty Quantification for Nuclear Density Functional Theory and Information Content of New Measurements text, January 2015

Applying Bayesian parameter estimation to relativistic heavy-ion collisions: simultaneous characterization of the initial state and quark-gluon plasma medium text, January 2016

Constraints on rapidity-dependent initial conditions from charged particle pseudorapidity densities and two-particle correlations text, January 2016

Error Analysis in Nuclear Density Functional Theory text, January 2014

Using Statistical and Computer Models to Quantify Volcanic Hazards
journal, November 2009

Inverse Problem Theory and Methods for Model Parameter Estimation
book, January 2005

Error analysis in nuclear density functional theory
journal, February 2015

Bayesian Computation and Stochastic Systems
journal, February 1995

Computer Model Calibration Using High-Dimensional Output
journal, June 2008

The Design and Analysis of Computer Experiments
book, January 2003

Efficient Emulators for Multivariate Deterministic Functions
journal, December 2008

Bayesian calibration of computer models
journal, August 2001

Screening, Predicting, and Computer Experiments
journal, February 1992

Cosmic Calibration
journal, July 2006

Nuclear energy density optimization
journal, August 2010

Bayesian analysis of computer code outputs: A tutorial
journal, October 2006

Probabilistic sensitivity analysis of complex models: a Bayesian approach
journal, August 2004

A Framework for Validation of Computer Models
journal, May 2007

Combining Field Data and Computer Simulations for Calibration and Prediction
journal, January 2004

Design and Analysis of Computer Experiments
journal, November 1989

Modeling Data from Computer Experiments: An Empirical Comparison of Kriging with MARS and Projection Pursuit Regression
journal, October 2007

Equation of State Calculations by Fast Computing Machines
journal, June 1953

Orthogonal Array-Based Latin Hypercubes
journal, December 1993

Monte Carlo sampling methods using Markov chains and their applications
journal, April 1970

Nuclear energy density optimization: Large deformations
journal, February 2012

Functional Data Analysis
book, January 2005

Orthogonal Array-Based Latin Hypercubes
journal, December 1993

Design and analysis of computer experiments
conference, September 1998

The Design and Analysis of Computer Experiments
book, January 2018

Design and analysis of computer experiments
journal, December 2010

Functional Data Analysis
book, January 2005

First Results from the CARIBU Facility: Mass Measurements on the r-Process Path
text, January 2013

Error Analysis in Nuclear Density Functional Theory
text, January 2014

Cosmic Calibration
text, January 2006

Bayesian estimation of the specific shear and bulk viscosity of quark–gluon plasma
journal, August 2019

Error analysis in nuclear density functional theory
journal, February 2015

Bayesian parameter estimation for effective field theories
journal, May 2016

Control functionals for Monte Carlo integration
journal, May 2016

Error Analysis in Nuclear Density Functional Theory
text, January 2014

Control functionals for Monte Carlo integration
preprint, January 2014

Uncertainty Quantification for Nuclear Density Functional Theory and Information Content of New Measurements
text, January 2015

Applying Bayesian parameter estimation to relativistic heavy-ion collisions: simultaneous characterization of the initial state and quark-gluon plasma medium
text, January 2016

Constraints on rapidity-dependent initial conditions from charged particle pseudorapidity densities and two-particle correlations
text, January 2016

Error Analysis in Nuclear Density Functional Theory
text, January 2014