Sequential Bayesian Experimental Design for Calibration of Expensive Simulation Models

References

• Azimi, J., Fern, A., and Fern, X. (2010), “Batch Bayesian Optimization via Simulation Matching,” in Advances in Neural Information Processing Systems . Red Hook, NY, USA: Curran Associates Inc.
   Google Scholar
• Bayarri, M. J., Berger, J. O., and Liu, F. (2009), “Modularization in Bayesian Analysis, with Emphasis on Analysis of Computer Models,” Bayesian Analysis, 4, 119–150. DOI: 10.1214/09-BA404.
   Web of Science ®Google Scholar
• 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. DOI: 10.1198/004017007000000092.
   Web of Science ®Google Scholar
• Binois, M., Huang, J., Gramacy, R. B., and Ludkovski, M. (2019), “Replication or Exploration? Sequential Design for Stochastic Simulation Experiments,” Technometrics, 61, 7–23. DOI: 10.1080/00401706.2018.1469433.
   Web of Science ®Google Scholar
• Chipman, H. A., George, E. I., and McCulloch, R. E. (2010), “BART: Bayesian Additive Regression Trees,” The Annals of Applied Statistics, 4, 266–298. DOI: 10.1214/09-AOAS285.
   Web of Science ®Google Scholar
• Damblin, G., Barbillon, P., Keller, M., Pasanisi, A., and Parent, E. “Adaptive Numerical Designs for the Calibration of Computer Codes,” SIAM/ASA Journal on Uncertainty Quantification, 6, 151–179. DOI: 10.1137/15M1033162.
   Web of Science ®Google Scholar
• Fernández, F. L., Martino, L., Elvira, V., Delgado, D., and López-Santiago, J. (2020), “Adaptive Quadrature Schemes for Bayesian Inference via Active Learning,” IEEE Access, 8, 208462–208483. DOI: 10.1109/ACCESS.2020.3038333.
   Web of Science ®Google Scholar
• Frazier, P. I. (2018), “A Tutorial on Bayesian Optimization,” available at https://arxiv.org/abs/1807.02811
   Google Scholar
• Gelman, A., Carlin, J. B., Stern, H. S., and Rubin, D. B. Bayesian Data Analysis (2nd ed.), London: Chapman and Hall/CRC.
   Google Scholar
• Ginsbourger, D., Le Riche, R., and Carraro, L. (2010), “Kriging is Well-Suited to Parallelize Optimization,” in Computational Intelligence in Expensive Optimization Problems, eds. Y. Tenne, C. K. Goh, pp. 131–162, Berlin: Springer.
   Google Scholar
• Gramacy, R. B. (2020), Surrogates: Gaussian Process Modeling, Design, and Optimization for the Applied Sciences, New York, NY: CRC Press; Taylor & Francis Group.
   Google Scholar
• Gu, M., and Wang, L. (2018), “Scaled Gaussian Stochastic Process for Computer Model Calibration and Prediction,” SIAM/ASA Journal on Uncertainty Quantification, 6, 1555–1583. DOI: 10.1137/17M1159890.
   Web of Science ®Google Scholar
• Higdon, D., Gattiker, J., Williams, B., and Rightley, M. (2008), “Computer Model Calibration Using High-Dimensional Output,” Journal of the American Statistical Association, 103, 570–583. DOI: 10.1198/016214507000000888.
   Web of Science ®Google Scholar
• 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. DOI: 10.1137/S1064827503426693.
   Web of Science ®Google Scholar
• Huang, J., Gramacy, R. B., Binois, M., and Libraschi, M. (2020), “On-site Surrogates for Large-Scale Calibration,” Applied Stochastic Models in Business and Industry, 36, 283–304. DOI: 10.1002/asmb.2523.
   Web of Science ®Google Scholar
• Järvenpää, M., Gutmann, M. U., Pleska, A., Vehtari, A., and Marttinen, P. (2019), “Efficient Acquisition Rules for Model-Based Approximate Bayesian Computation,” Bayesian Analysis, 14, 595–622. DOI: 10.1214/18-BA1121.
   Web of Science ®Google Scholar
• Jones, D. R., Schonlau, M., and Welch, W. J. (1998), “Efficient Global Optimization of Expensive Black-Box Functions,” Journal of Global Optimization, 13, 455–492. DOI: 10.1023/A:1008306431147.
   Web of Science ®Google Scholar
• Joseph, V. R., Dasgupta, T., Tuo, R., and Wu, C. F. J. (2015), “Sequential Exploration of Complex Surfaces Using Minimum Energy Designs,” Technometrics, 57, 64–74. DOI: 10.1080/00401706.2014.881749.
   Web of Science ®Google Scholar
• Joseph, V. R., Wang, D., Gu, L., Lyu, S., and Tuo, R. (2019), “Deterministic Sampling of Expensive Posteriors Using Minimum Energy Designs,” Technometrics, 61, 297–308. DOI: 10.1080/00401706.2018.1552203.
   Web of Science ®Google Scholar
• Kandasamy, K., Schneider, J., and Póczos, B. (2015), “Bayesian Active Learning for Posterior Estimation,” in Proceedings of the 24th International Conference on Artificial Intelligence, IJCAI’15, pp. 3605–3611, AAAI Press.
   Google Scholar
• Kennedy, M. C., and O’Hagan, A. (2001), “Bayesian Calibration of Computer Models,” Journal of the Royal Statistical Society, Series B, 63, 425–464. DOI: 10.1111/1467-9868.00294.
   Web of Science ®Google Scholar
• Koermer, S., Loda, J., Noble, A., and Gramacy, R. B. (2023), “Active Learning for Simulator Calibration,” available at https://arxiv.org/abs/2301.10228
   Google Scholar
• Lovell, A. E. (2018), “A Deeper Understanding of the Inputs for Reaction Theory Through Uncertainty Quantification,” Ph.D. thesis. Michigan State University.
   Google Scholar
• Lovell, A. E., Nunes, F. M., Sarich, J., and Wild, S. M. (2017), “Uncertainty Quantification for Optical Model Parameters,” Physical Review C, 95, 024611. DOI: 10.1103/PhysRevC.95.024611.
   Web of Science ®Google Scholar
• Marin, J.-M., Pudlo, P., Robert, C. P., and Ryder, R. J. (2012), “Approximate Bayesian Computational Methods,” Statistics and Computing, 22, 1167–1180. DOI: 10.1007/s11222-011-9288-2.
   Web of Science ®Google Scholar
• O’Hagan, A. (1991), “Bayes–Hermite Quadrature,” Journal of Statistical Planning and Inference, 29, 245–260.
   Web of Science ®Google Scholar
• Plumlee, M. (2017), “Bayesian Calibration of Inexact Computer Models,” Journal of the American Statistical Association, 112, 1274–1285. DOI: 10.1080/01621459.2016.1211016.
   Web of Science ®Google Scholar
• Rasmussen, C. E., and Williams, C. K. I. (2005), Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning), Cambridge, MA: The MIT Press.
   Google Scholar
• Sacks, J., Welch, W. J., Mitchell, T. J., and Wynn, H. P. (1989), “Statistical Science,” Design and Analysis of Computer Experiments, 4, 409–423. DOI: 10.1214/ss/1177012413.
   Google Scholar
• Santner, T. J., Williams, B. J., and Notz, W. I. (2018), The Design and Analysis of Computer Experiments. Springer Series in Statistics (2nd ed), New York: Springer.
   Google Scholar
• Sauer, A., Gramacy, R. B., and Higdon, D. (2023), “Active Learning for Deep Gaussian Process Surrogates,” Technometrics, 65, 4–18. DOI: 10.1080/00401706.2021.2008505.
   Web of Science ®Google Scholar
• Seo, S., Wallat, M., Graepel, T., and Obermayer, K. (2000), “Gaussian Process Regression: Active Data Selection and Test Point Rejection.” Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium, 3, 241–246 (2000).
   Google Scholar
• Sürer, O., Nunes, F. M., Plumlee, M., and Wild, S. M. (2022), “Uncertainty Quantification in Breakup Reactions,” Physical Review C, 106, 024607. DOI: 10.1103/PhysRevC.106.024607.
   Web of Science ®Google Scholar
• Sürer, O., Plumlee, M., and Wild, S. M. (2022), “Parallel Implementation of Novel Uncertainty Quantification Methods (PUQ) Python Package,” available at https://github.com/parallelUQ/PUQ, Accessed: 2022-09-18.
   Google Scholar
• Sürer, O., Plumlee, M., and Wild, S. M. (2023), “Supplementary materials to ‘Sequential Bayesian Experimental Design for Calibration of Expensive Simulation Models’,” Technometrics.
   Web of Science ®Google Scholar
• Thompson, I. J. (1988), “Coupled Reaction Channels Calculations in Nuclear Physics,” Computer Physics Reports, 7, 167–212. DOI: 10.1016/0167-7977(88)90005-6.
   Web of Science ®Google Scholar
• Yang, H., Sürer, O., Duque, D., Morton, D. P., Singh, B., Fox, S. J., Pasco, R., Pierce, K., Rathouz, P., Valencia, V., Du, Z., Pignone, M., Escott, M. E., Adler, S. I., Johnston, S. C., and Meyers, L. A. (2021), “Design of COVID-19 Staged Alert Systems to Ensure Healthcare Capacity with Minimal Closures,” Nature Communications, 12, 3767. DOI: 10.1038/s41467-021-23989-x.
   PubMed Web of Science ®Google Scholar