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Filip Tronarp
Filip Tronarp
Associate Senior Lecturer / Assistant Professor, Lund University
Verified email at matstat.lu.se - Homepage
Title
Cited by
Cited by
Year
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
882019
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
662018
Maximum likelihood estimation and uncertainty quantification for Gaussian process approximation of deterministic functions
T Karvonen, G Wynne, F Tronarp, C Oates, S Särkkä
SIAM/ASA Journal on Uncertainty Quantification 8 (3), 926-958, 2020
592020
Bayesian ode solvers: The maximum a posteriori estimate
F Tronarp, S Särkkä, P Hennig
Statistics and Computing 31 (3), 1-18, 2021
552021
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
452016
Calibrated adaptive probabilistic ODE solvers
N Bosch, P Hennig, F Tronarp
International Conference on Artificial Intelligence and Statistics, 3466-3474, 2021
352021
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
252019
Gaussian target tracking with direction-of-arrival von Mises–Fisher measurements
AF Garcia-Fernandez, F Tronarp, S Särkkä
IEEE Transactions on Signal Processing 67 (11), 2960-2972, 2019
252019
Student's -Filters for Noise Scale Estimation
F Tronarp, T Karvonen, S Särkkä
IEEE Signal Processing Letters 26 (2), 352-356, 2019
252019
The rank-reduced Kalman filter: Approximate dynamical-low-rank filtering in high dimensions
J Schmidt, P Hennig, J Nick, F Tronarp
Advances in Neural Information Processing Systems 36, 2024
192024
Fenrir: Physics-Enhanced Regression for Initial Value Problems
F Tronarp, N Bosch, P Hennig
International Conference on Machine Learning, 21776-21794, 2022
182022
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
182017
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
142018
Pick-and-mix information operators for probabilistic ODE solvers
N Bosch, F Tronarp, P Hennig
International Conference on Artificial Intelligence and Statistics, 10015-10027, 2022
122022
Variable Splitting Methods for Constrained State Estimation in Partially Observed Markov Processes
R Gao, F Tronarp, S Särkkä
IEEE Signal Processing Letters 27, 1305-1309, 2020
122020
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
122018
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
122018
Gaussian Process Classification Using Posterior Linearization
ÁF García-Fernández, F Tronarp, S Särkkä
IEEE Signal Processing Letters 26 (5), 735-739, 2019
112019
Parallel-in-Time Probabilistic Numerical ODE Solvers
N Bosch, A Corenflos, F Yaghoobi, F Tronarp, P Hennig, S Särkkä
Journal of Machine Learning Research 25 (206), 1-27, 2024
102024
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
102019
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Articles 1–20