| Sigma-point filtering for nonlinear systems with non-additive heavy-tailed noise F Tronarp, R Hostettler, S Särkkä 2016 19th International Conference on Information Fusion (FUSION), 1859-1866, 2016 | 21 | 2016 |
| Iterative Filtering and Smoothing in Nonlinear and Non-Gaussian Systems Using Conditional Moments F Tronarp, ÁF García-Fernández, S Särkkä IEEE Signal Processing Letters 25 (3), 408-412, 2018 | 14 | 2018 |
| Student-t process quadratures for filtering of non-linear systems with heavy-tailed noise J Prüher, F Tronarp, T Karvonen, S Särkkä, O Straka 2017 20th International Conference on Information Fusion (Fusion), 1-8, 2017 | 10 | 2017 |
| Probabilistic solutions to ordinary differential equations as nonlinear Bayesian filtering: a new perspective F Tronarp, H Kersting, S Särkkä, P Hennig Statistics and Computing 29 (6), 1297-1315, 2019 | 7 | 2019 |
| Iterative statistical linear regression for Gaussian smoothing in continuous-time non-linear stochastic dynamic systems F Tronarp, S Särkkä Signal Processing 159, 1-12, 2019 | 3 | 2019 |
| Gaussian Target Tracking With Direction-of-Arrival von Mises–Fisher Measurements ÁF García-Fernández, F Tronarp, S Särkkä IEEE Transactions on Signal Processing 67 (11), 2960-2972, 2019 | 3 | 2019 |
| Iterated Extended Kalman Smoother-Based Variable Splitting for -Regularized State Estimation R Gao, F Tronarp, S Särkkä IEEE Transactions on Signal Processing 67 (19), 5078-5092, 2019 | 2 | 2019 |
| Tracking of dynamic functional connectivity from MEG data with Kalman filtering F Tronarp, NP Subramaniyam, S Särkkä, L Parkkonen 2018 40th Annual International Conference of the IEEE Engineering in …, 2018 | 2 | 2018 |
| Expectation–maximization algorithm with a nonlinear Kalman smoother for MEG/EEG connectivity estimation NP Subramaniyam, F Tronarp, S Särkkä, L Parkkonen EMBEC & NBC 2017, 763-766, 2017 | 2 | 2017 |
| Regularized State Estimation And Parameter Learning Via Augmented Lagrangian Kalman Smoother Method R Gao, F Tronarp, Z Zhao, S Särkä 2019 IEEE 29th International Workshop on Machine Learning for Signal …, 2019 | 1 | 2019 |
| Asymptotics of Maximum Likelihood Parameter Estimates For Gaussian Processes: The Ornstein–Uhlenbeck Prior T Karvonen, F Tronarp, S Särkkä 2019 IEEE 29th International Workshop on Machine Learning for Signal …, 2019 | 1 | 2019 |
| Gaussian Process Classification Using Posterior Linearization ÁF García-Fernández, F Tronarp, S Särkkä IEEE Signal Processing Letters 26 (5), 735-739, 2019 | 1 | 2019 |
| Student's -Filters for Noise Scale Estimation F Tronarp, T Karvonen, S Särkkä IEEE Signal Processing Letters 26 (2), 352-356, 2019 | 1 | 2019 |
| Combined Analysis-L1 and Total Variation ADMM with Applications to MEG Brain Imaging and Signal Reconstruction R Gao, F Tronarp, S Särkkä 2018 26th European Signal Processing Conference (EUSIPCO), 1930-1934, 2018 | 1 | 2018 |
| Continuous-Discrete von Mises-Fisher Filtering on S2for Reference Vector Tracking F Tronarp, R Hostettler, S Särkkä 2018 21st International Conference on Information Fusion (FUSION), 1345-1352, 2018 | 1 | 2018 |
| Modeling the Drift Function in Stochastic Differential Equations using Reduced Rank Gaussian Processes R Hostettler, F Tronarp, S Särkkä IFAC-PapersOnLine 51 (15), 778-783, 2018 | 1 | 2018 |
| Maximum likelihood estimation and uncertainty quantification for Gaussian process approximation of deterministic functions T Karvonen, G Wynne, F Tronarp, CJ Oates, S Särkkä arXiv preprint arXiv:2001.10965, 2020 | | 2020 |
| Importance Densities for Particle Filtering Using Iterated Conditional Expectations R Hostettler, F Tronarp, ÁF García-Fernández, S Särkkä IEEE Signal Processing Letters 27, 211-215, 2020 | | 2020 |
| Iterative and Geometric Methods for State Estimation in Non-linear Models F Tronarp Aalto University, 2020 | | 2020 |
| Probabilistic solutions to ordinary differential equations as nonlinear Bayesian filtering F Tronarp, H Kersting, S Särkkä, P Hennig SPRINGER, 2019 | | 2019 |
Simo SärkkäAssociate Professor, Aalto UniversityVerified email at aalto.fi
