Sequential Monte Carlo methods for system identification TB Schön, F Lindsten, J Dahlin, J Wågberg, CA Naesseth, A Svensson, ... IFAC-PapersOnLine 48 (28), 775-786, 2015 | 105 | 2015 |
Maximum likelihood identification of stable linear dynamical systems J Umenberger, J Wågberg, IR Manchester, TB Schön Automatica 96, 280-292, 2018 | 40 | 2018 |
Prediction performance after learning in Gaussian process regression J Wagberg, D Zachariah, T Schon, P Stoica Artificial Intelligence and Statistics, 1264-1272, 2017 | 24 | 2017 |
Nonlinear system identification: Learning while respecting physical models using a sequential monte carlo method A Wigren, J Wågberg, F Lindsten, AG Wills, TB Schön IEEE Control Systems Magazine 42 (1), 75-102, 2022 | 17 | 2022 |
Reliable semi-supervised learning when labels are missing at random X Liu, D Zachariah, J Wågberg, TB Schön arXiv preprint arXiv:1811.10947, 2018 | 8 | 2018 |
On identification via EM with latent disturbances and Lagrangian relaxation J Umenberger, J Wågberg, IR Manchester, TB Schön IFAC-PapersOnLine 48 (28), 69-74, 2015 | 8 | 2015 |
Bayesian nonparametric identification of piecewise affine ARX systems J Wågberg, F Lindsten, TB Schön IFAC-PapersOnLine 48 (28), 709-714, 2015 | 7 | 2015 |
Regularized parametric system identification: a decision-theoretic formulation J Wågberg, D Zachariah, TB Schön 2018 Annual American Control Conference (ACC), 1895-1900, 2018 | 6 | 2018 |
Ssgmi greenely report energy disaggregation with low resolution data M Al Abassi, K Johnsson, J Karlsson, TB Schön, J Wågberg Energy 3, 5, 2015 | 3 | 2015 |
Linear system identification via em with latent disturbances and lagrangian relaxation J Umenberger, J Wågberg, IR Manchester, TB Schön arXiv preprint arXiv:1603.09157, 2016 | 2 | 2016 |
54 Low-Rank Tensor Decompositions for Nonlinear System Identification KIM BATSELIER, A WIGREN, J WÅGBERG, F LINDSTEN, AG WILLS, ... IEEE CONTROL SYSTEMS, 2022 | | 2022 |
Continuous Occupancy Mapping Using Gaussian Processes J Wågberg, E Walldén Viklund | | 2012 |