| 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 | 105 | 2015 |
| Particle Metropolis–Hastings using gradient and Hessian information J Dahlin, F Lindsten, TB Schön Statistics and computing 25, 81-92, 2015 | 54 | 2015 |
| Ensemble approaches for improving community detection methods J Dahlin, P Svenson arXiv preprint arXiv:1309.0242, 2013 | 39 | 2013 |
| Accelerating pseudo-marginal Metropolis-Hastings by correlating auxiliary variables J Dahlin, F Lindsten, J Kronander, TB Schön arXiv preprint arXiv:1511.05483, 2015 | 34 | 2015 |
| 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 | 28 | 2012 |
| Getting started with particle Metropolis-Hastings for inference in nonlinear dynamical models J Dahlin, TB Schön Journal of Statistical Software 88, 1-41, 2019 | 27 | 2019 |
| 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 | 24 | 2015 |
| Particle filter-based Gaussian process optimisation for parameter inference J Dahlin, F Lindsten IFAC Proceedings Volumes 47 (3), 8675-8680, 2014 | 23 | 2014 |
| A method for community detection in uncertain networks J Dahlin, P Svenson 2011 European Intelligence and Security Informatics Conference, 155-162, 2011 | 23 | 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 | 21 | 2014 |
| Particle Metropolis Hastings using Langevin dynamics J Dahlin, F Lindsten, T Schon Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International …, 2013 | 17 | 2013 |
| Second-order particle MCMC for Bayesian parameter inference J Dahlin, F Lindsten, TB Schön IFAC Proceedings Volumes 47 (3), 8656-8661, 2014 | 16 | 2014 |
| 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 | 15 | 2015 |
| Accurate Gaussian mixture model smoothing using a two-filter approach MP Balenzuela, J Dahlin, N Bartlett, AG Wills, C Renton, B Ninness 2018 IEEE Conference on Decision and Control (CDC), 694-699, 2018 | 14 | 2018 |
| Sparse Bayesian ARX models with flexible noise distributions J Dahlin, A Wills, B Ninness IFAC-PapersOnLine 51 (15), 25-30, 2018 | 14 | 2018 |
| Getting started with particle Metropolis-Hastings for inference in nonlinear dynamical models J Dahlin, TB Schön arXiv preprint arXiv:1511.01707, 2015 | 14 | 2015 |
| Hierarchical Bayesian ARX models for robust inference J Dahlin, F Lindsten, TB Schön, A Wills System Identification 16 (1), 131-136, 2012 | 14 | 2012 |
| Quasi-Newton particle Metropolis-Hastings J Dahlin, F Lindsten, TB Schön IFAC-PapersOnLine 48 (28), 981-986, 2015 | 12 | 2015 |
| On robust input design for nonlinear dynamical models PE Valenzuela, J Dahlin, CR Rojas, TB Schön Automatica 77, 268-278, 2017 | 11 | 2017 |
| 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 | 11 | 2014 |
Thomas SchönProfessor, Uppsala University在 it.uu.se 的电子邮件经过验证
