Sigma-point filtering for nonlinear systems with non-additive heavy-tailed noise. - Filip Tronarp; Roland Hostettler; Simo Särkkä (FUSION, 2016)

Student-t process quadratures for filtering of non-linear systems with heavy-tailed noise. - Jakub Prüher; Filip Tronarp; Toni Karvonen; Simo Särkkä; Ondrej Straka (FUSION, 2017)

Tracking of dynamic functional connectivity from MEG data with Kalman filtering. - Filip Tronarp; Narayan Puthanmadam Subramaniyam; Simo Särkkä; Lauri Parkkonen (EMBC, 2018)

Combined Analysis-L1 and Total Variation ADMM with Applications to MEG Brain Imaging and Signal Reconstruction. - Rui Gao; Filip Tronarp; Simo Särkkä (EUSIPCO, 2018)

Continuous-Discrete von Mises-Fisher Filtering on S2 for Reference Vector Tracking. - Filip Tronarp; Roland Hostettler; Simo Särkkä (FUSION, 2018)

Non-Linear Continuous-Discrete Smoothing by Basis Function Expansions of Brownian Motion. - Filip Tronarp; Simo Särkkä (FUSION, 2018)

Mixture Representation of the MatéRn class with Applications in State Space Approximations and Bayesian quadrature. - Filip Tronarp; Toni Karvonen; Simo Särkkä (MLSP, 2018)

Gaussian process classification using posterior linearisation. - Ángel F. García-Fernández; Filip Tronarp; Simo Särkkä (CoRR, 2018)

Iterative Filtering and Smoothing in Nonlinear and Non-Gaussian Systems Using Conditional Moments. - Filip Tronarp; Ángel F. García-Fernández; Simo Särkkä (IEEE Signal Process. Lett., 2018)

Joint Calibration of Inertial Sensors and Magnetometers using von Mises-Fisher Filtering and Expectation Maximization. - Roland Hostettler; Ángel F. García-Fernández; Filip Tronarp; Simo Särkkä (FUSION, 2019)

Updates in Bayesian Filtering by Continuous Projections on a Manifold of Densities. - Filip Tronarp; Simo Särkkä (ICASSP, 2019)

Regularized State Estimation And Parameter Learning Via Augmented Lagrangian Kalman Smoother Method. - Rui Gao; Filip Tronarp; Zheng Zhao 004; Simo Särkkä (MLSP, 2019)

Asymptotics of Maximum Likelihood Parameter Estimates For Gaussian Processes: The Ornstein-Uhlenbeck Prior. - Toni Karvonen; Filip Tronarp; Simo Särkkä (MLSP, 2019)

Probabilistic solutions to ordinary differential equations as nonlinear Bayesian filtering: a new perspective. - Filip Tronarp; Hans Kersting; Simo Särkkä; Philipp Hennig (Stat. Comput., 2019)

Iterative statistical linear regression for Gaussian smoothing in continuous-time non-linear stochastic dynamic systems. - Filip Tronarp; Simo Särkkä (Signal Process., 2019)

Gaussian Process Classification Using Posterior Linearization. - Ángel F. García-Fernández; Filip Tronarp; Simo Särkkä (IEEE Signal Process. Lett., 2019)

Student's $t$-Filters for Noise Scale Estimation. - Filip Tronarp; Toni Karvonen; Simo Särkkä (IEEE Signal Process. Lett., 2019)

Iterated Extended Kalman Smoother-Based Variable Splitting for L1-Regularized State Estimation. - Rui Gao; Filip Tronarp; Simo Särkkä (IEEE Trans. Signal Process., 2019)

Gaussian Target Tracking With Direction-of-Arrival von Mises-Fisher Measurements. - Ángel F. García-Fernández; Filip Tronarp; Simo Särkkä (IEEE Trans. Signal Process., 2019)

State-Space Gaussian Process for Drift Estimation in Stochastic Differential Equations. - Zheng Zhao 004; Filip Tronarp; Roland Hostettler; Simo Särkkä (ICASSP, 2020)

Maximum Likelihood Estimation and Uncertainty Quantification for Gaussian Process Approximation of Deterministic Functions. - Toni Karvonen; George Wynne; Filip Tronarp; Chris J. Oates; Simo Särkkä (SIAM/ASA J. Uncertain. Quantification, 2020)

Variable Splitting Methods for Constrained State Estimation in Partially Observed Markov Processes. - Rui Gao; Filip Tronarp; Simo Särkkä (IEEE Signal Process. Lett., 2020)

Importance Densities for Particle Filtering Using Iterated Conditional Expectations. - Roland Hostettler; Filip Tronarp; Ángel F. García-Fernández; Simo Särkkä (IEEE Signal Process. Lett., 2020)

Calibrated Adaptive Probabilistic ODE Solvers. - Nathanael Bosch; Philipp Hennig; Filip Tronarp (AISTATS, 2021)

A Probabilistic Taylor Expansion with Applications in Filtering and Differential Equations. - Toni Karvonen; Jon Cockayne; Filip Tronarp; Simo Särkkä (CoRR, 2021)

Bayesian ODE solvers: the maximum a posteriori estimate. - Filip Tronarp; Simo Särkkä; Philipp Hennig (Stat. Comput., 2021)

Pick-and-Mix Information Operators for Probabilistic ODE Solvers. - Nathanael Bosch; Filip Tronarp; Philipp Hennig (AISTATS, 2022)

Continuous-Discrete Filtering and Smoothing on Submanifolds of Euclidean Space. - Filip Tronarp; Simo Särkkä (FUSION, 2022)

Fenrir: Physics-Enhanced Regression for Initial Value Problems. - Filip Tronarp; Nathanael Bosch; Philipp Hennig (ICML, 2022)

Probabilistic Exponential Integrators. - Nathanael Bosch; Philipp Hennig; Filip Tronarp (NeurIPS, 2023)

The Rank-Reduced Kalman Filter: Approximate Dynamical-Low-Rank Filtering In High Dimensions. - Jonathan Schmidt 001; Philipp Hennig; Jörg Nick; Filip Tronarp (NeurIPS, 2023)

Computing the matrix exponential and the Cholesky factor of a related finite horizon Gramian. - Tony Stillfjord; Filip Tronarp (CoRR, 2023)

A probabilistic Taylor expansion with Gaussian processes. - Toni Karvonen; Jon Cockayne; Filip Tronarp; Simo Särkkä (Trans. Mach. Learn. Res., 2023)

Numerically Robust Square Root Implementations of Statistical Linear Regression Filters and Smoothers. - Filip Tronarp (EUSIPCO, 2024)

Probabilistic ODE solvers for integration error-aware numerical optimal control. - Amon Lahr; Filip Tronarp; Nathanael Bosch; Jonathan Schmidt 001; Philipp Hennig; Melanie N. Zeilinger (L4DC, 2024)

Orthonormal expansions for translation-invariant kernels. - Filip Tronarp; Toni Karvonen (J. Approx. Theory, 2024)

Parallel-in-Time Probabilistic Numerical ODE Solvers. - Nathanael Bosch; Adrien Corenflos; Fatemeh Yaghoobi; Filip Tronarp; Philipp Hennig; Simo Särkkä (J. Mach. Learn. Res., 2024)

The two filter formula reconsidered: Smoothing in partially observed Gauss-Markov models without information parametrization. - Filip Tronarp (CoRR, 2025)

Propagating Model Uncertainty through Filtering-based Probabilistic Numerical ODE Solvers. - Dingling Yao; Filip Tronarp; Nathanael Bosch (CoRR, 2025)

Numerically robust Gaussian state estimation with singular observation noise. - Nicholas Krämer; Filip Tronarp (CoRR, 2025)