| Particle Gibbs with ancestor sampling F Lindsten, MI Jordan, TB Schön The Journal of Machine Learning Research 15 (1), 2145-2184, 2014 | 379 | 2014 |
| Machine learning: a first course for engineers and scientists A Lindholm, N Wahlström, F Lindsten, TB Schön Cambridge University Press, 2022 | 345 | 2022 |
| Evaluating model calibration in classification J Vaicenavicius, D Widmann, C Andersson, F Lindsten, J Roll, T Schön The 22nd international conference on artificial intelligence and statistics …, 2019 | 336 | 2019 |
| Backward simulation methods for Monte Carlo statistical inference F Lindsten, TB Schön Foundations and Trends® in Machine Learning 6 (1), 1-143, 2013 | 215 | 2013 |
| Bayesian inference and learning in Gaussian process state-space models with particle MCMC R Frigola, F Lindsten, TB Schön, CE Rasmussen Advances in neural information processing systems 26, 2013 | 210 | 2013 |
| Clustering using sum-of-norms regularization: With application to particle filter output computation F Lindsten, H Ohlsson, L Ljung 2011 IEEE Statistical Signal Processing Workshop (SSP), 201-204, 2011 | 167 | 2011 |
| Calibration tests in multi-class classification: A unifying framework D Widmann, F Lindsten, D Zachariah Advances in neural information processing systems 32, 2019 | 148 | 2019 |
| Elements of sequential monte carlo CA Naesseth, F Lindsten, TB Schön Foundations and Trends® in Machine Learning 12 (3), 187-306, 2019 | 146 | 2019 |
| 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 | 116 | 2015 |
| Coherent modulation of the sea-level annual cycle in the United States by Atlantic Rossby waves FM Calafat, T Wahl, F Lindsten, J Williams, E Frajka-Williams Nature communications 9 (1), 2571, 2018 | 107 | 2018 |
| Just relax and come clustering!: A convexification of k-means clustering F Lindsten, H Ohlsson, L Ljung Linköping University Electronic Press, 2011 | 101 | 2011 |
| Nested sequential monte carlo methods C Naesseth, F Lindsten, T Schon International Conference on Machine Learning, 1292-1301, 2015 | 99 | 2015 |
| Sequential kernel herding: Frank-Wolfe optimization for particle filtering S Lacoste-Julien, F Lindsten, F Bach Artificial Intelligence and Statistics, 544-552, 2015 | 97 | 2015 |
| Recursive maximum likelihood identification of jump Markov nonlinear systems E Özkan, F Lindsten, C Fritsche, F Gustafsson IEEE Transactions on Signal Processing 63 (3), 754-765, 2014 | 88 | 2014 |
| Markovian score climbing: Variational inference with KL (p|| q) C Naesseth, F Lindsten, D Blei Advances in Neural Information Processing Systems 33, 15499-15510, 2020 | 81 | 2020 |
| Divide-and-conquer with sequential Monte Carlo F Lindsten, AM Johansen, CA Naesseth, B Kirkpatrick, TB Schön, ... Journal of Computational and Graphical Statistics 26 (2), 445-458, 2017 | 80 | 2017 |
| Ancestor sampling for particle Gibbs F Lindsten, T Schön, M Jordan Advances in Neural Information Processing Systems 25, 2012 | 80 | 2012 |
| An efficient stochastic approximation EM algorithm using conditional particle filters F Lindsten 2013 IEEE international conference on acoustics, speech and signal …, 2013 | 76 | 2013 |
| Smoothing with couplings of conditional particle filters PE Jacob, F Lindsten, TB Schön Journal of the American Statistical Association, 2020 | 73 | 2020 |
| Bayesian semiparametric Wiener system identification F Lindsten, TB Schön, MI Jordan Automatica 49 (7), 2053-2063, 2013 | 71 | 2013 |
Thomas SchönProfessor, Uppsala UniversityVerified email at it.uu.se
