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Batch nonlinear continuous-time trajectory estimation as exactly sparse Gaussian process regression., , , and . Auton. Robots, 39 (3): 221-238 (2015)Non-Linear Continuous-Discrete Smoothing by Basis Function Expansions of Brownian Motion., and . FUSION, page 1-8. IEEE, (2018)Continuous-Discrete von Mises-Fisher Filtering on S2 for Reference Vector Tracking., , and . FUSION, page 1345-1352. IEEE, (2018)Cooperative Localization Using Posterior Linearization Belief Propagation., , and . IEEE Trans. Vehicular Technology, 67 (1): 832-836 (2018)Student's $t$-Filters for Noise Scale Estimation., , and . IEEE Signal Process. Lett., 26 (2): 352-356 (2019)State-Space Inference for Non-Linear Latent Force Models with Application to Satellite Orbit Prediction., , and . ICML, icml.cc / Omnipress, (2012)State Space Methods for Efficient Inference in Student-t Process Regression., and . AISTATS, volume 38 of JMLR Workshop and Conference Proceedings, JMLR.org, (2015)Infinite-Dimensional Kalman Filtering Approach to Spatio-Temporal Gaussian Process Regression., and . AISTATS, volume 22 of JMLR Proceedings, page 993-1001. JMLR.org, (2012)Worst-case optimal approximation with increasingly flat Gaussian kernels., and . CoRR, (2019)Gaussian Process Classification Using Posterior Linearization., , and . IEEE Signal Process. Lett., 26 (5): 735-739 (2019)