Andreas Lindholm (Svensson)
Andreas Lindholm (Svensson)
Machine Learning Research Engineer, Annotell
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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
A flexible state space model for learning nonlinear dynamical systems
A Svensson, TB Schön
Automatica 80, 189-199, 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
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
Machine Learning: A First Course for Engineers and Scientists
A Lindholm, N Wahlström, F Lindsten, TB Schön
Cambridge University Press, 2022
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
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
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
Supervised machine learning
A Lindholm, N Wahlström, F Lindsten, TB Schön
Department of Information Technology, Uppsala University: Uppsala, Sweden, 112, 2019
Nonlinear state space smoothing using the conditional particle filter
A Svensson, TB Schön, M Kok
17th IFAC Symposium on System Identification, 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
Probabilistic modeling–linear regression & Gaussian processes
F Lindsten, TB Schön, A Svensson, N Wahlström
Uppsala: Uppsala University 7, 2017
Data consistency approach to model validation
A Lindholm, D Zachariah, P Stoica, TB Schön
IEEE Access 7, 59788-59796, 2019
Particle Filter Explained without Equations
A Svensson
Oct, 2013
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, Sweden, 2019
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
Learning dynamical systems with particle stochastic approximation em
A Lindholm, F Lindsten
arXiv preprint arXiv:1806.09548, 2018
Statistical machine learning
F Lindsten, N Wahlström, A Svensson, TB Schön
Lecture note. Department of Information Technology, Uppsala University. url …, 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
Comparing two recent particle filter implementations of Bayesian system identification
A Svensson, TB Schön
Technical Report 2016, 2016
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