Andreas Lindholm (Svensson)
Andreas Lindholm (Svensson)
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TitleCited byYear
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
412015
A flexible state space model for learning nonlinear dynamical systems
A Svensson, TB Schön
Automatica 80, 189-199, 2016
292016
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
212016
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
92014
Nonlinear state space smoothing using the conditional particle filter
A Svensson, TB Schön, M Kok
17th IFAC Symposium on System Identification, 2015
72015
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
62018
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
62018
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
52018
Marginalizing Gaussian process hyperparameters using sequential Monte Carlo
A Svensson, J Dahlin, TB Schön
Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2015 …, 2015
42015
Nonlinear state space model identification using a regularized basis function expansion
A Svensson, TB Schön, A Solin, S Särkkä
Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2015 …, 2015
22015
Learning probabilistic models of dynamical phenomena using particle filters
A Svensson
Uppsala University, 2016
12016
Some details on state space smoothing using the conditional particle filter
A Svensson, TB Schön, M Kok
Technical Report 2015-019, Department of Information Technology, Uppsala …, 2015
12015
Data Consistency Approach to Model Validation
A Svensson, D Zachariah, P Stoica, TB Schön
arXiv preprint arXiv:1808.05889, 2018
2018
Learning dynamical systems with particle stochastic approximation EM
A Svensson, F Lindsten
arXiv preprint arXiv:1806.09548, 2018
2018
Machine learning with state-space models, Gaussian processes and Monte Carlo methods
A Svensson
Acta Universitatis Upsaliensis, 2018
2018
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
2018
How consistent is my model with the data? Information-Theoretic Model Check
A Svensson, D Zachariah, TB Schön
arXiv preprint arXiv:1712.02675, 2017
2017
Model Predictive Control with Invariant Sets in Artificial Pancreas for Type 1 Diabetes Mellitus
A Svensson
2013
Automatic Generation of Control Code for Flexible Automation
A Svensson
2012
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Articles 1–19