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References
Abdalla N, Banerjee S, Ramachandran G, Arnold S (2020) Bayesian state space modeling of physical processes in industrial hygiene. Technometrics 62(2):147–160. https://doi.org/10.1080/00401706.2019.1630009
Collins WD, Rasch PJ, Boville BA, Hack JJ, McCaa JR, Williamson DL, Kiehl JT, Briegleb B (2004) Description of the NCAR Community Atmosphere Model (CAM 3.0). Technical Report TN-464+STR, National Center for Atmorspheric Research. https://www.cesm.ucar.edu/models/atm-cam/docs/description/description.pdf
Datta A, Banerjee S, Finley AO, Hamm NAS, Schaap M (2016) Non-separable dynamic nearest-neighbor gaussian process models for large spatio-temporal data with an application to particulate matter analysis. Ann Appl Stat 10:1286–1316. https://doi.org/10.1214/16-AOAS931
Frankenburg I, Banerjee S (2022) Dynamic Bayesian learning and calibration of spatiotemporal mechanistic systems. arXiv: 2208.06528v2
Genton MG, Kleiber W (2015) Cross-covariance functions for multivariate geostatistics. Stat Sci, 147–163
Gramacy R (2016) laGP: large-scale spatial modeling via local approximate gaussian processes in R. J Stat Softw 72(1):1–46
Gramacy RB, Haaland B (2016) Speeding up neighborhood search in local Gaussian process prediction. Technometrics 58(3):294–303. https://doi.org/10.1080/00401706.2015.1027067
Gramacy RB, Niemi J, Weiss RM (2014) Massively parallel approximate Gaussian process regression. arxiv:1310.5182
Higdon D, Gattiker J, Williams B, Rightley M (2008) Computer model calibration using high-dimensional output. J Am Stat Assoc 103(482):570–583. https://doi.org/10.1198/016214507000000888
Katzfuss M, Stroud JR, Wikle CK (2020) Ensemble Kalman methods for high-dimensional hierarchical dynamic space-time models. J Am Stat Assoc 115(530):866–885. https://doi.org/10.1080/01621459.2019.1592753
Kennedy MC, O’Hagan A (2001) Bayesian calibration of computer models. J R Stat Soc Series B Stat Methodol 63(3):425–464. https://doi.org/10.1111/1467-9868.00294
Monteiro JVD, Banerjee S, Ramachandran G (2014) Bayesian modeling for physical processes in industrial hygiene using misaligned workplace data. Technometrics 56(2):238–247. https://doi.org/10.1080/00401706.2013.836988
Wikle CK, Hooten MB (2010) A general science-based framework for dynamical spatio-temporal models. TEST 19(3):417–451
Zhang L, Banerjee S (2022) Spatial factor modeling: a Bayesian matrix-normal approach for misaligned data. Biometrics 78(2):560–573. https://doi.org/10.1111/biom.13452
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Banerjee, S. Discussion of “Saving Storage in Climate Ensembles: A Model-Based Stochastic Approach” by Huang Huang, Stefano Castruccio, Allison H. Baker and Marc Genton. JABES 28, 365–369 (2023). https://doi.org/10.1007/s13253-023-00541-6
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DOI: https://doi.org/10.1007/s13253-023-00541-6