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Fredrik Lindsten
Fredrik Lindsten
Associate Professor, Linköping University
Bestätigte E-Mail-Adresse bei liu.se - Startseite
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Zitiert von
Zitiert von
Jahr
Particle Gibbs with ancestor sampling
F Lindsten, MI Jordan, TB Schon
Journal of Machine Learning Research 15, 2145-2184, 2014
3462014
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
2512019
Machine learning: a first course for engineers and scientists
A Lindholm, N Wahlström, F Lindsten, TB Schön
Cambridge University Press, 2022
2292022
Backward simulation methods for Monte Carlo statistical inference
F Lindsten, TB Schön
Foundations and Trends® in Machine Learning 6 (1), 1-143, 2013
1982013
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
1802013
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
1372011
Just relax and come clustering!: A convexification of k-means clustering
F Lindsten, H Ohlsson, L Ljung
Linköping University Electronic Press, 2011
1202011
Calibration tests in multi-class classification: A unifying framework
D Widmann, F Lindsten, D Zachariah
Advances in neural information processing systems 32, 2019
1142019
Elements of sequential monte carlo
CA Naesseth, F Lindsten, TB Schön
Foundations and Trends® in Machine Learning 12 (3), 307-392, 2019
1092019
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
1062015
Nested sequential monte carlo methods
C Naesseth, F Lindsten, T Schon
International Conference on Machine Learning, 1292-1301, 2015
912015
Sequential kernel herding: Frank-Wolfe optimization for particle filtering
S Lacoste-Julien, F Lindsten, F Bach
Artificial Intelligence and Statistics, 544-552, 2015
892015
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
802018
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
782014
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
732017
An efficient stochastic approximation EM algorithm using conditional particle filters
F Lindsten
2013 IEEE international conference on acoustics, speech and signal …, 2013
712013
Ancestor sampling for particle Gibbs
F Lindsten, T Schön, M Jordan
Advances in Neural Information Processing Systems 25, 2012
692012
Smoothing with couplings of conditional particle filters
PE Jacob, F Lindsten, TB Schön
Journal of the American Statistical Association, 2020
672020
Uniform ergodicity of the particle Gibbs sampler
F Lindsten, R Douc, E Moulines
Scandinavian Journal of Statistics 42 (3), 775-797, 2015
672015
Bayesian semiparametric Wiener system identification
F Lindsten, TB Schön, MI Jordan
Automatica 49 (7), 2053-2063, 2013
642013
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