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Inverse Problem for a Time-Series Valued Computer Simulator via Scalarization
OM & QT, Indian Institute of Management Indore, Indore, India
Department of Mathematics & Statistics, Acadia University, Wolfville, Canada
Department of Mathematics & Statistics, Acadia University, Wolfville, Canada
OM & QT, Indian Institute of Management Indore, Indore, India
- 1 OM & QT, Indian Institute of Management Indore, Indore, India
- 2 Department of Mathematics & Statistics, Acadia University, Wolfville, Canada
- 3 Department of Mathematics & Statistics, Acadia University, Wolfville, Canada
- 4 OM & QT, Indian Institute of Management Indore, Indore, India
Open Journal of Statistics·Volume 06 (2016)·Pages 528–544·Published 8 June 2016·DOI10.4236/ojs.2016.63045
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Abstract
For an expensive to evaluate computer simulator, even the estimate of the overall surface can be a challenging problem. In this paper, we focus on the estimation of the inverse solution, i.e., to find the set(s) of input combinations of the simulator that generates a pre-determined simulator output. Ranjan et al. [1] proposed an expected improvement criterion under a sequential design framework for the inverse problem with a scalar valued simulator. In this paper, we focus on the inverse problem for a time-series valued simulator. We have used a few simulated and two real examples for performance comparison.
KeywordsCalibrationComputer ExperimentsContour EstimationGaussian Process ModelNon-Stationary ProcessSequential Design
- Ranjan, P., Bingham, D. and Michailidis, G. (2008) Sequential Experiment Design for Contour Estimation from Complex Computer Codes. Technometrics, 50, 527-541. http://dx.doi.org/10.1198/004017008000000541
- Teismann, H., Karsten, R., Hammond, R., Hardman, J. and Franklin, J. (2009) On the Possibility of Counter-Productive Intervention: The Population Mean for Blowflies Models Can Be an Increasing Function of the Death Rate. Journal of Biological Systems, 47, 739-757. http://dx.doi.org/10.1142/S0218339009003009
- Sacks, J., Welch, W.J., Mitchell, T.J. and Wynn, H.P. (1989) Design and Analysis of Computer Experiments. Statistical Science, 4, 409-423. http://dx.doi.org/10.1214/ss/1177012413
- Santner, T.J., Williams, B.J. and Notz, W.I. (2003) The Design and Analysis of Computer Experiments. Springer Verlag, New York. http://dx.doi.org/10.1007/978-1-4757-3799-8
- Jones, D., Schonlau, M. and Welch, W. (1998) Efficient Global Optimization of Expensive Black-Box Functions. Journal of Global Optimization, 13, 455-492. http://dx.doi.org/10.1023/A:1008306431147
- Bingham, D., Ranjan, P. and Welch, W. (2014) Sequential Design of Computer Experiments for Optimization, Estimating Contours, and Related Objectives. Statistics in Action: A Canadian Outlook, 109-124.
- Chipman, H.A., George, E.I. and McCulloch, R.E. (2010) BART: Bayesian Additive Regression Trees. Annals of Applied Statistics, 4, 266-298. http://dx.doi.org/10.1214/09-AOAS285
- Chipman, H., Ranjan, P. and Wang, W. (2012) Sequential Design for Computer Experiments with a Flexible Bayesian Additive Model. Canadian Journal of Statistics, 40, 663-678. http://dx.doi.org/10.1002/cjs.11156
- Rasmussen, C.E. and Williams, C.K.I. (2006) Gaussian Processes for Machine Learning. The MIT Press, Cambridge.
- Ranjan, P., Haynes, R. and Karsten, R. (2011) A Computationally Stable Approach to Gaussian Process Interpolation of Deterministic Computer Simulation Data. Technometrics, 53, 366-378. http://dx.doi.org/10.1198/TECH.2011.09141
- Dancik, G.M. and Dorman, K.S. (2008) mlegp: Statistical Analysis for Computer Models of Biological Systems Using R. Bioinformatics, 24, 1966-1967. http://dx.doi.org/10.1093/bioinformatics/btn329
- MacDonald, K.B., Ranjan, P. and Chipman, H. (2015) GPfit: An R Package for Fitting a Gaussian Process Model to Deterministic Simulator Outputs. Journal of Statistical Software, 64, 1-23. http://dx.doi.org/10.18637/jss.v064.i12