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
A flexible state space model for learning nonlinear dynamical systems
A Svensson, TB Schön
Automatica 80, 189-199, 2016
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
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
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
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
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
Nonlinear state space smoothing using the conditional particle filter
A Svensson, TB Schön, M Kok
17th IFAC Symposium on System Identification, 2015
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
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
Learning dynamical systems with particle stochastic approximation EM
A Svensson, F Lindsten
arXiv preprint arXiv:1806.09548, 2018
Machine learning with state-space models, Gaussian processes and Monte Carlo methods
A Svensson
Acta Universitatis Upsaliensis, 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
Learning probabilistic models of dynamical phenomena using particle filters
A Svensson
Uppsala University, 2016
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
Data Consistency Approach to Model Validation
A Lindholm, D Zachariah, P Stoica, TB Schön
IEEE Access 7, 59788-59796, 2019
How consistent is my model with the data? Information-Theoretic Model Check
A Svensson, D Zachariah, TB Schön
IFAC-PapersOnLine 51 (15), 407-412, 2018
Model Predictive Control with Invariant Sets in Artificial Pancreas for Type 1 Diabetes Mellitus
A Svensson
Automatic Generation of Control Code for Flexible Automation
A Svensson
Music classification mini project: Instructions
A Lindholm, D Widmann
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