Filip Tronarp
Title
Cited by
Cited by
Year
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
222016
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
172018
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
112017
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
92019
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
42019
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
42019
Student's -Filters for Noise Scale Estimation
F Tronarp, T Karvonen, S Särkkä
IEEE Signal Processing Letters 26 (2), 352-356, 2019
42019
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
32019
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
32018
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
32017
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
22019
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
22018
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
22018
Bayesian ODE Solvers: The Maximum A Posteriori Estimate
F Tronarp, S Sarkka, P Hennig
arXiv preprint arXiv:2004.00623, 2020
12020
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
12020
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
12019
Updates in Bayesian Filtering by Continuous Projections on a Manifold of Densities
F Tronarp, S Särkkä
ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and …, 2019
12019
Gaussian Process Classification Using Posterior Linearization
ÁF García-Fernández, F Tronarp, S Särkkä
IEEE Signal Processing Letters 26 (5), 735-739, 2019
12019
Mixture representation of the Matérn class with applications in state space approximations and Bayesian quadrature
F Tronarp, T Karvonen, S Särkkä
2018 IEEE 28th International Workshop on Machine Learning for Signal …, 2018
12018
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
12018
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Articles 1–20