[5] Cappé, O., Moulines, E., and Ryden, T. (2005). Inference in Hidden Markov Models. Springer Series in Statistics. Springer.
[6] Carrassi, A., Bocquet, M., Bertino, L., and Evensen, G. (2018). Data assimilation in the geosciences: An overview of methods, issues, and perspectives. Wiley Interdisciplinary Reviews: Climate Change, 9(5).
[7] Carron, A., Todescato, M., Carli, R., Schenato, L., and Pillonetto, G. (2016). Machine learning meets Kalman filtering. In 2016 IEEE 55th conference on decision and control (CDC), pages 4594-4599. IEEE.
[8] Ceruti, G. and Lubich, C. (2022). An unconventional robust integrator for dynamical low-rank approximation. BIT. Numerical Mathematics, 62(1):23-44.
[9] Chada, N. K., Stuart, A. M., and Tong, X. T. (2020). Tikhonov regularization within ensemble Kalman inversion. SIAM Journal on Numerical Analysis, 58(2):1263-1294.
[10] Cressie, N., Shi, T., and Kang, E. L. (2010). Fixed Rank Filtering for Spatio-Temporal Data. Journal of Computational and Graphical Statistics, 19(3):724-745.
[11] Evensen, G. (1994). Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. Journal of Geophysical Research: Oceans, 99(C5):10143–10162.
[12] Evensen, G. (2009a). Data assimilation: the ensemble Kalman filter, volume 2. Springer.
[13] Evensen, G. (2009b). The ensemble Kalman filter for combined state and parameter estimation. IEEE Control Systems Magazine, 29(3):83-104.
[14] Evensen, G. (2021). Formulating the history matching problem with consistent error statistics. Computational Geosciences, 25(3).
[15] Farrell, B. F. and Ioannou, P. J. (2001). State estimation using a reduced-order kalman filter. Journal of the Atmospheric Sciences, 58(23):3666-3680.
[16] Galka, A., Yamashita, O., Ozaki, T., Biscay, R., and Valdés-Sosa, P. (2004). A solution to the dynamical inverse problem of EEG generation using spatiotemporal Kalman filtering. NeuroImage, 23(2):435-453.
[17] Ghil, M. and Malanotte-Rizzoli, P. (1991). Data assimilation in meteorology and oceanography. In Advances in geophysics, volume 33, pages 141-266. Elsevier.
[18] Golub, G. H. and Van Loan, C. F. (2013). Matrix computations. JHU press.
[19] Grewal, M. and Andrews, A. (2011). Kalman Filtering: Theory and Practice Using MATLAB. Wiley.
[20] Hamelijnck, O., Wilkinson, W., Loppi, N., Solin, A., and Damoulas, T. (2021). Spatio-temporal variational Gaussian processes. Advances in Neural Information Processing Systems, 34:23621-23633.
[21] Hamill, T. M. and Snyder, C. (2000). A hybrid ensemble Kalman filter-3D variational analysis scheme. Monthly Weather Review, 128(8):2905-2919.
[22] Hensman, J., Fusi, N., and Lawrence, N. D. (2013). Gaussian processes for big data. In Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence (UAI), pages 282-290.
[23] Imperial College London (2020). Londonair - London air quality network (LAQN). https: //www.londonair.org.uk.
[24] Jones, D., Wang, W., and Fawcett, R. (2009). High-quality spatial climate data-sets for Australia. Australian Meteorological and Oceanographic Journal, 58(04):233-248.
[25] Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Transactions of the ASME-Journal of Basic Engineering, 82(Series D):35-45.