Bayesian filtering and smoothing S Särkkä Cambridge University Press, 2013 | 2866* | 2013 |
On unscented Kalman filtering for state estimation of continuous-time nonlinear systems S Sarkka IEEE Transactions on automatic control 52 (9), 1631-1641, 2007 | 704 | 2007 |
Recursive noise adaptive Kalman filtering by variational Bayesian approximations S Sarkka, A Nummenmaa IEEE Transactions on Automatic control 54 (3), 596-600, 2009 | 684 | 2009 |
Applied stochastic differential equations S Särkkä, A Solin Cambridge University Press, 2019 | 574 | 2019 |
Rao-Blackwellized particle filter for multiple target tracking S Sarkka, A Vehtari, J Lampinen Information Fusion 8 (1), 2-15, 2007 | 386 | 2007 |
Unscented rauch--tung--striebel smoother S Särkkä IEEE transactions on automatic control 53 (3), 845-849, 2008 | 374 | 2008 |
Optimal filtering with Kalman filters and smoothers–a Manual for Matlab toolbox EKF/UKF J Hartikainen, A Solin, S Särkkä Biomedical Engineering, 1-57, 2008 | 351* | 2008 |
Kalman filtering and smoothing solutions to temporal Gaussian process regression models J Hartikainen, S Särkkä 2010 IEEE international workshop on machine learning for signal processing …, 2010 | 298 | 2010 |
Spatiotemporal learning via infinite-dimensional Bayesian filtering and smoothing: A look at Gaussian process regression through Kalman filtering S Särkkä, A Solin, J Hartikainen IEEE Signal Processing Magazine 30 (4), 51-61, 2013 | 284 | 2013 |
Hilbert space methods for reduced-rank Gaussian process regression A Solin, S Särkkä Statistics and Computing 30 (2), 419-446, 2020 | 250 | 2020 |
Recursive Bayesian inference on stochastic differential equations S Särkkä Dissertation Abstracts International, 2006 | 210 | 2006 |
A survey of Monte Carlo methods for parameter estimation D Luengo, L Martino, M Bugallo, V Elvira, S Särkkä EURASIP Journal on Advances in Signal Processing 2020, 1-62, 2020 | 182 | 2020 |
Recursive outlier-robust filtering and smoothing for nonlinear systems using the multivariate Student-t distribution R Piché, S Särkkä, J Hartikainen 2012 IEEE International Workshop on Machine Learning for Signal Processing, 1-6, 2012 | 179 | 2012 |
Dynamic retrospective filtering of physiological noise in BOLD fMRI: DRIFTER S Särkkä, A Solin, A Nummenmaa, A Vehtari, T Auranen, S Vanni, FH Lin NeuroImage 60 (2), 1517-1527, 2012 | 161 | 2012 |
Sensors and AI techniques for situational awareness in autonomous ships: A review S Thombre, Z Zhao, H Ramm-Schmidt, JMV García, T Malkamäki, ... IEEE transactions on intelligent transportation systems 23 (1), 64-83, 2020 | 147 | 2020 |
Linear operators and stochastic partial differential equations in Gaussian process regression S Särkkä Artificial Neural Networks and Machine Learning–ICANN 2011: 21st …, 2011 | 144 | 2011 |
Modeling and interpolation of the ambient magnetic field by Gaussian processes A Solin, M Kok, N Wahlström, TB Schön, S Särkkä IEEE Transactions on robotics 34 (4), 1112-1127, 2018 | 140 | 2018 |
Batch Continuous-Time Trajectory Estimation as Exactly Sparse Gaussian Process Regression. TD Barfoot, CH Tong, S Särkkä Robotics: Science and Systems 10, 1-10, 2014 | 140 | 2014 |
Posterior linearization filter: Principles and implementation using sigma points ÁF García-Fernández, L Svensson, MR Morelande, S Särkkä IEEE transactions on signal processing 63 (20), 5561-5573, 2015 | 132 | 2015 |
Gaussian filtering and smoothing for continuous-discrete dynamic systems S Särkkä, J Sarmavuori Signal Processing 93 (2), 500-510, 2013 | 124 | 2013 |