Probabilistic formulations for transferring inferences from mathematical models to physical systems

SIAM Journal on Scientific Computing

Volumn 26, Issue 2, 2005, Pages 467-487

  Goldstein, Michael ^a   Rougier, Jonathan ^a  

^a DURHAM UNIVERSITY   (United Kingdom)

Author keywords

Bayesian inference; Calibrated prediction; Calibration; Direct simulator; History matching; Indirect simulator; Measurable inputs; Top simulator; Tuning inputs; Uncertainty analysis

Indexed keywords

CALIBRATION; COMPUTER SIMULATION; MATHEMATICAL MODELS; SENSITIVITY ANALYSIS; SIMULATORS; UNCERTAIN SYSTEMS;

BAYESIAN INFERENCE; CALIBRATED PREDICTION; DIRECT SIMULATOR; HISTORY MATCHING; INDIRECT SIMULATOR; MEASURABLE INPUTS; TOP SIMULATOR; TUNING INPUTS; UNCERTAINTY ANALYSIS;

PROBABILISTIC LOGICS;

EID: 16244396056     PISSN: 10648275     EISSN: None     Source Type: Journal    
DOI: 10.1137/S106482750342670X     Document Type: Article

References (17)

1. J. BERNARDO AND A. SMITH, Bayesian Theory, John Wiley & Sons, Chichester, UK, 1994.
2. D. B. CHELTON, Physical oceanography: A brief overview for statisticians, Statist. Sci., 9 (1994), pp. 150-166.
3. A. COLLING, Ocean Circulation, Butterworth-Heinemann, Boston, 2001.
4. R. COWELL, A. DAWID, S. LAURITZEN, AND D. SPIEGELHALTER, Probabilistic Networks and Expert Systems, Springer-Verlag, New York, 1999.
5. P. CRAIG, M. GOLDSTEIN, J. ROUGIER, AND A. SEHEULT, Bayesian forecasting for complex systems using computer simulators, J. Amer. Statist. Assoc., 96 (2001), pp. 717-729.
6. P. CRAIG, M. GOLDSTEIN, A. SEHEULT, AND J. SMITH, Pressure matching for hydrocarbon reservoirs: A case study in the use of Bayes linear strategies for large computer experiments, in Case Studies in Bayesian Statistics, Vol. 3, C. Gatsonis, J. Hodges, R. Kass, R. McCulloch, P. Rossi, and N. Singpurwalla, eds., Lecture Notes in Statist. 121, Springer-Verlag, New York, 1997, pp. 37-93, with discussion.
7. P. CRAIG, M. GOLDSTEIN, A. SEHEULT, AND J. SMITH, Constructing partial prior specifications for models of complex physical systems, The Statistician, 47 (1998), pp. 37-53, with discussion.
8. N. A. C. CRESSIE, Statistics for Spatial Data, John Wiley & Sons, New York, 1991.
9. J. HOUGHTON, Y. DING, D. GRIGGS, M. NOGUER, P. VAN DE LINDEN, X. DAI, K. MASKELL, AND C. JOHNSON, EDS., Climate Change 2001: The Scientific Basis. Contribution of Working Group 1 to the Third Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, UK, 2001.
10. F. JENSEN, Bayesian Networks and Decision Graphs, Springer-Verlag, New York, 2001.
11. M. KENNEDY AND A. O'HAGAN, Bayesian calibration of computer models, J. R. Stat. Soc. Ser. B Stat. Methodol., 63 (2001), pp. 425-464, with discussion.
12. NATIONAL RESEARCH COUNCIL (NRC), Report on statistics and physical oceanography, Statist. Sci., 9 (1994), pp. 167-221, with discussion.
13. J. PEIXOTO AND A. OORT, Physics of Climate, Springer-Verlag, New York, 1992.
14. C. ROBERT AND G. CASELLA, Monte Carlo Statistical Methods, Springer-Verlag, New York, 1999.
15. J. SACHS, W. WELCH, T. MITCHELL, AND H. WYNN, Design and analysis of computer experiments, Statist. Sci., 4 (1989), pp. 409-435, with discussion.
16. G. THOMAS, Principles of Hydrocarbon Reservoir Simulation, International Human Resources Development Corporation, Boston, 1982.
17. K. ZICKFELD, T. SLAWIG, AND S. RAHMSTORF, A low-order model for the response of the Atlantic thermohaline circulation to climate change, Ocean Dynamics, 54 (2004), pp. 8-26.