https://orcid.org/0000-0002-2989-965X
References
- Kennedy MC, O'Hagan A. Bayesian calibration of computer models. J R Stat Soc Ser B (Stat Methodol). 2001;63(3):425–464.
- Arendt PD, Apley DW, Chen W. Quantification of model uncertainty: calibration, model discrepancy, and identifiability. J Mech Des. 2012;134(10):100908.
- Bayarri M, Berger J, Cafeo J, et al. Computer model validation with functional output. Ann Statist. 2007;35(5):1874–1906.
- Bhat KS, Haran M, Goes M, et al. Computer model calibration with multivariate spatial output: a case study. In: Frontiers of statistical decision making and Bayesian analysis. 2010. p. 168–184.
- Higdon D, Gattiker J, Williams B, et al. Computer model calibration using high-dimensional output. J Amer Statist Assoc. 2008;103(482):570–583.
- Liu F, Bayarri M, Berger J. Modularization in Bayesian analysis, with emphasis on analysis of computer models. Bayesian Anal. 2009;4(1):119–150.
- Rumsey K, Huerta G, Brown J, et al. Dealing with measurement uncertainties as nuisance parameters in Bayesian model calibration. SIAM/ASA J Uncertain Quan. 2020;8(4):1287–1309.
- Brown JL, Hund LB. Estimating material properties under extreme conditions by using Bayesian model calibration with functional outputs. J R Stat Soc Ser C (Appl Statist). 2018;67(4):1023–1045.
- Pratola MT, Higdon DM. Bayesian additive regression tree calibration of complex high-dimensional computer models. Technometrics. 2016;58(2):166–179.
- Shah A, Wilson A, Ghahramani Z. Student-t processes as alternatives to gaussian processes. Artificial intelligence and statistics. 2014. p. 877–885.
- Kejzlar V, Maiti T. Variational inference with vine copulas: an efficient approach for Bayesian computer model calibration. Preprint, 2020. arXiv:2003.12890.
- Tsilifis P, Bilionis I, Katsounaros I, et al. Computationally efficient variational approximations for Bayesian inverse problems. J Verif Valid Uncertain Quan. 2016;1(3).
- Karvonen T, Kanagawa M, Särkkä S. On the positivity and magnitudes of Bayesian quadrature weights. Stat Comput. 2019;29(6):1317–1333.
- Llorente F, Martino L, Elvira V, et al. Adaptive quadrature schemes for Bayesian inference via active learning. Preprint, 2020. arXiv:2006.00535.
- Sambridge M. Geophysical inversion with a neighbourhood algorithm – I. searching a parameter space. Geophys J Int. 1999;138(2):479–494.
- Villagran A, Huerta G, Vannucci M, et al. Non-sampling approximation via voronoi tesselations. Comm Statist Simul Comput. 2016;45(2):717–736.
- Ying H, Mao K, Mosegaard K. Moving target Monte Carlo. Preprint, 2020. arXiv:2003.04873.
- Golub GH, Van Loan CF. Matrix computations. Vol. 3. Baltimore: JHU Press; 2012.
- Arendt PD, Apley DW, Chen W, et al. Improving identifiability in model calibration using multiple responses. J Mech Des. 2012;134(10).
- Osthus D, Gattiker J, Priedhorsky R, et al. Dynamic Bayesian influenza forecasting in the united states with hierarchical discrepancy (with discussion). Bayesian Anal. 2019;14(1):261–312.