References
- Bates, D. M., and Watts, D. G. (1988), Nonlinear Regression: Iterative Estimation and Linear Approximations, Wiley Online Library, available at onlinelibrary.wiley.com/doi/10.1002/9780470316757.fmatter/pdf.
- Bayarri, M. J., Berger, J. O., Paulo, R., Sacks, J., Cafeo, J. A., Cavendish, J., Lin, C.-H., and Tu, J. (2007), “A Framework for Validation of Computer Models,” Technometrics, 49, 138–154.
- Bouchard, G., and Triggs, B. (2004), “The Tradeoff Between Generative and Discriminative Classifiers,” in 16th IASC International Symposium on Computational Statistics (COMPSTAT’04), pp. 721–728.
- Clancy, C. E., and Rudy, Y. (1999), “Linking a Genetic Defect to its Cellular Phenotype in a Cardiac Arrhythmia,” Nature, 400, 566–569.
- Cressie, N. A. C. (1993), Statistics for Spatial Data, New York: Wiley.
- Farah, M., Birrell, P., Conti, S., and Angelis, D. D. (2014), “Bayesian Emulation and Calibration of a Dynamic Epidemic Model for A/H1N1 Influenza,” Journal of the American Statistical Association, 109, 1398–1411.
- Gelman, A., Carlin, J. B., Stern, H. S., and Rubin, D. B. (2014), Bayesian Data Analysis ( 3rd ed.), Boca Raton, FL: CRC Press.
- Geman, S., and Geman, D. (1984), “Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, 721–741.
- Gneiting, T., and Raftery, A. E. (2007), “Strictly Proper scoring Rules, Prediction, and Estimation,” Journal of the American Statistical Associationx, 102, 359–378.
- Goldstein, M., and Rougier, J. (2004), “Probabilistic Formulations for Transferring Inferences from Mathematical Models to Physical Systems,” SIAM Journal on Scientific Computing, 26, 467–487.
- Gramacy, R. B., Bingham, D., Holloway, J. P., Grosskopf, M. J., Kuranz, C. C., Rutter, E., Trantham, M., Drake, P. R., et al. (2015), “Calibrating a Large Computer Experiment Simulating Radiative Shock Hydrodynamics,” The Annals of Applied Statistics, 9, 1141–1168.
- Han, G., Santner, T. J., and Rawlinson, J. J. (2009), “Simultaneous Determination of Tuning and Calibration Parameters for Computer Experiments,” Technometrics, 51, 464–474.
- Higdon, D., Kennedy, M., Cavendish, J. C., Cafeo, J. A., and Ryne, R. D. (2004), “Combining Field Data and Computer Simulations for Calibration and Prediction,” SIAM Journal on Scientific Computing, 26, 448–466.
- Joseph, V. R., and Melkote, S. N. (2009), “Statistical Adjustments to Engineering Models,” Journal of Quality Technology, 41, 362–375.
- Joseph, V. R., and Yan, H. (2015), “Engineering-Driven Statistical Adjustment and Calibration,” Technometrics, 57, 257–267.
- Kennedy, M. C., and O’Hagan, A. (2001a), “Bayesian Calibration of Computer Models,” Journal of the Royal Statistical Society, Series B, 63, 425–464.
- ——— (2001b), “Supplementary Details on Bayesian Calibration of Computer Models,” Tech. Rep., Internal Report. Available at http://www.shef.ac.uk/∼st1ao/ps/calsup.ps.
- Kleiber, W., Sain, S. R., Heaton, M. J., Wiltberger, M., Reese, C. S., and Bingham, D. (2013), “Parameter Tuning for a Multi-Fidelity Dynamical Model of the Magnetosphere,” The Annals of Applied Statistics, 7, 1286–1310.
- Liang, P., and Jordan, M. I. (2008), “An Asymptotic Analysis of Generative, Discriminative, and Pseudolikelihood Estimators,” in,” Proceedings of the 25th International Conference on Machine Learning, ACM, pp. 584–591.
- Liu, F., Bayarri, M., Berger, J., et al. (2009), “Modularization in Bayesian Analysis, With Emphasis on Analysis of Computer Models,” Bayesian Analysis, 4, 119–150.
- Morris, M. D., Mitchell, T. J., and Ylvisaker, D. (1993), “Bayesian Design and Analysis of Computer Experiments: Use of Derivatives in Surface Prediction,” Technometrics, 35, 243–255.
- Plumlee, M. (2014), “Fast Prediction of Deterministic Functions Using Sparse Grid Experimental Designs,” Journal of the American Statistical Association, 109, 1581–1591.
- Plumlee, M., and Joseph, V. R. (2015), “Orthogonal Gaussian Process Models,” Under review.
- Plumlee, M., Joseph, V. R., and Yang, H. (2016), “Calibrating Functional Parameters in the Ion Channel Models of Cardiac Cells,” Journal of the American Statistical Association, 111, 500–509.
- Qian, P. Z., and Wu, C. J. (2008), “Bayesian Hierarchical Modeling for Integrating Low-Accuracy and High-Accuracy Experiments,” Technometrics, 50, 192–204.
- Rasmussen, C. E., and Williams, C. (2006), Gaussian Processes for Machine Learning, Cambridge, MA: MIT Press.
- Santner, T. J., Williams, B. J., and Notz, W. (2003), The Design and Analysis of Computer Experiments, New York: Springer Science & Business Media.
- Shapiro, A., Dentcheva, D., and Ruszczynski, A. (2014), Lectures on Stochastic Programming: Modeling and Theory, Philadelphia, PA: SIAM.
- Storlie, C. B., Lane, W. A., Ryan, E. M., Gattiker, J. R., and Higdon, D. M. (2015), “Calibration of Computational Models With Categorical Parameters and Correlated Outputs via Bayesian Smoothing Spline ANOVA,” Journal of the American Statistical Association, 110, 68–82.
- Tuo, R., and Wu, C. F. J. (2015a), “Efficient Calibration for Imperfect Computer Models,” The Annals of Statistics, 43, 2331–2352.
- Tuo, R., and Wu, C. F. J. (2015b), “A Theoretical Framework for Calibration in Computer Models: Parametrization, Estimation and Convergence Properties,” arXiv:1508.07155.
- Wang, S., Chen, W., and Tsui, K.-L. (2009), “Bayesian Validation of Computer Models,” Technometrics, 51, 439–451.
- Wendland, H. (2004), Scattered Data Approximation, Cambridge, UK: Cambridge University Press.