| Machine Learning: A First Course for Engineers and Scientists A Lindholm, N Wahlström, F Lindsten, TB Schön Cambridge University Press, 2022 | 216 | 2022 |
| Probabilistic forecasting of electricity consumption, photovoltaic power generation and net demand of an individual building using Gaussian Processes DW Van der Meer, M Shepero, A Svensson, J Widén, J Munkhammar Applied energy 213, 195-207, 2018 | 156 | 2018 |
| A flexible state space model for learning nonlinear dynamical systems A Svensson, TB Schön Automatica 80, 189-199, 2016 | 121 | 2016 |
| Sequential Monte Carlo Methods for System Identification TB Schön, F Lindsten, J Dahlin, J Wågberg, CA Naesseth, A Svensson, ... 17th IFAC Symposium on System Identification, 975-980, 2015 | 105 | 2015 |
| Computationally efficient Bayesian learning of Gaussian process state space models A Svensson, A Solin, S Särkkä, TB Schön 19th International Conference on Artificial Intelligence and Statistics …, 2016 | 62 | 2016 |
| Probabilistic learning of nonlinear dynamical systems using sequential Monte Carlo TB Schön, A Svensson, L Murray, F Lindsten Mechanical systems and signal processing 104, 866-883, 2018 | 42 | 2018 |
| Identification of jump Markov linear models using particle filters A Svensson, TB Schön, F Lindsten IEEE 53rd Annual Conference on Decision and Control (CDC) (Los Angeles, CA …, 2014 | 28 | 2014 |
| Supervised machine learning A Lindholm, N Wahlström, F Lindsten, TB Schön Department of Information Technology, Uppsala University: Uppsala, Sweden, 112, 2019 | 27 | 2019 |
| Marginalizing Gaussian process hyperparameters using sequential Monte Carlo A Svensson, J Dahlin, TB Schön 2015 IEEE 6th International Workshop on Computational Advances in Multi …, 2015 | 24 | 2015 |
| Nonlinear state space smoothing using the conditional particle filter A Svensson, TB Schön, M Kok 17th IFAC Symposium on System Identification, 2015 | 22 | 2015 |
| Learning of state-space models with highly informative observations: A tempered sequential Monte Carlo solution A Svensson, TB Schön, F Lindsten Mechanical systems and signal processing 104, 915-928, 2018 | 14 | 2018 |
| Probabilistic modeling–linear regression & Gaussian processes F Lindsten, TB Schön, A Svensson, N Wahlström Uppsala: Uppsala University 7, 2017 | 13 | 2017 |
| Supervised Machine Learning, Lecture notes for the Statistical Machine Learning course A Lindholm, N Wahlström, F Lindsten, TB Schön Department of Information Technology, Uppsala University, 2019 | 12 | 2019 |
| Learning dynamical systems with particle stochastic approximation EM A Lindholm, F Lindsten arXiv preprint arXiv:1806.09548, 2018 | 11* | 2018 |
| Identification of a Duffing oscillator using particle Gibbs with ancestor sampling TJ Rogers, TB Schön, A Lindholm, K Worden, EJ Cross Journal of Physics: Conference Series 1264 (1), 012051, 2019 | 9 | 2019 |
| Data consistency approach to model validation A Lindholm, D Zachariah, P Stoica, TB Schön IEEE Access 7, 59788-59796, 2019 | 9 | 2019 |
| Learning nonlinear state-space models using smooth particle-filter-based likelihood approximations A Svensson, F Lindsten, TB Schön IFAC-PapersOnLine 51 (15), 652-657, 2018 | 7 | 2018 |
| Particle filter explained without equations A Svensson Oct, 2013 | 7 | 2013 |
| Machine Learning Research A Svensson, A Solin, S Särkkä, T Schön Proceedings of the 19th International Conference on Artificial intelligence …, 2016 | 6 | 2016 |
| Nonlinear state space model identification using a regularized basis function expansion A Svensson, TB Schön, A Solin, S Särkkä 2015 IEEE 6th International Workshop on Computational Advances in Multi …, 2015 | 6 | 2015 |
Thomas SchönProfessor, Uppsala UniversityVerified email at it.uu.se
