Johan Dahlin
Johan Dahlin
Self-employed consultant in Bayesian statistics and Machine learning.
Verified email at - Homepage
TitleCited byYear
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
Particle Metropolis–Hastings using gradient and Hessian information
J Dahlin, F Lindsten, TB Schön
Statistics and computing 25 (1), 81-92, 2015
Ensemble approaches for improving community detection methods
J Dahlin, P Svenson
arXiv preprint arXiv:1309.0242, 2013
Combining entity matching techniques for detecting extremist behavior on discussion boards
J Dahlin, F Johansson, L Kaati, C Mårtenson, P Svenson
2012 IEEE/ACM International Conference on Advances in Social Networks …, 2012
Accelerating pseudo-marginal Metropolis-Hastings by correlating auxiliary variables
J Dahlin, F Lindsten, J Kronander, TB Schön
arXiv preprint arXiv:1511.05483, 2015
Particle filter-based Gaussian process optimisation for parameter inference
J Dahlin, F Lindsten
IFAC Proceedings Volumes 47 (3), 8675-8680, 2014
A method for community detection in uncertain networks
J Dahlin, P Svenson
2011 European Intelligence and Security Informatics Conference, 155-162, 2011
Detecting and positioning overtaking vehicles using 1D optical flow
D Hultqvist, J Roll, F Svensson, J Dahlin, TB Schön
2014 IEEE Intelligent Vehicles Symposium Proceedings, 861-866, 2014
Particle Metropolis Hastings using Langevin dynamics
J Dahlin, F Lindsten, T Schon
Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International …, 2013
Hierarchical Bayesian ARX models for robust inference
J Dahlin, F Lindsten, TB Schön, A Wills
System Identification 16 (1), 131-136, 2012
Newton-based maximum likelihood estimation in nonlinear state space models
M Kok, J Dahlin, TB Schön, A Wills
IFAC-PapersOnLine 48 (28), 398-403, 2015
Second-order particle MCMC for Bayesian parameter inference
J Dahlin, F Lindsten, TB Schön
IFAC Proceedings Volumes 47 (3), 8656-8661, 2014
A graph/particle-based method for experiment design in nonlinear systems
PE Valenzuela, J Dahlin, CR Rojas, TB Schön
IFAC Proceedings Volumes 47 (3), 1404-1409, 2014
Getting started with particle Metropolis-Hastings for inference in nonlinear dynamical models
J Dahlin, TB Schön
arXiv preprint arXiv:1511.01707, 2015
Quasi-Newton particle Metropolis-Hastings
J Dahlin, F Lindsten, TB Schön
IFAC-PapersOnLine 48 (28), 981-986, 2015
Real-time video based lighting using GPU raytracing
J Kronander, J Dahlin, D Jönsson, M Kok, TB Schön, J Unger
2014 22nd European Signal Processing Conference (EUSIPCO), 1627-1631, 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
On robust input design for nonlinear dynamical models
PE Valenzuela, J Dahlin, CR Rojas, TB Schön
Automatica 77, 268-278, 2017
Backward sequential Monte Carlo for marginal smoothing
J Kronander, TB Schön, J Dahlin
2014 IEEE Workshop on Statistical Signal Processing (SSP), 368-371, 2014
Sequential Monte Carlo for inference in nonlinear state space models
J Dahlin
Linköping University Electronic Press, 2014
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