Johan Dahlin

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Thomas SchönProfessor, Uppsala UniversityDirección de correo verificada de it.uu.se
Adrian WillsUniversity of Newcastle AustraliaDirección de correo verificada de newcastle.edu.au
Pontus SvensonRISE SICSDirección de correo verificada de ri.se
B NinnessUniversity of Newcastle AustraliaDirección de correo verificada de newcastle.edu.au
Andreas Lindholm (Svensson)Machine Learning Research Engineer, KognicDirección de correo verificada de kognic.com
Joel KronanderHead of Synthetic Data at Scale AIDirección de correo verificada de liu.se
Mattias VillaniProfessor of Statistics, Stockholm UniversityDirección de correo verificada de stat.su.se
Christian A. NaessethAssistant Professor, University of AmsterdamDirección de correo verificada de uva.nl
Cristian R. RojasProfessor, Division of Decision and Control Systems, KTH Royal Institute of TechnologyDirección de correo verificada de kth.se
Manon KokAssociate Professor at the Delft Center for Systems and ControlDirección de correo verificada de tudelft.nl
Christian MårtensonFOIDirección de correo verificada de foi.se
Fredrik JohanssonSenior Data Scientist @ Swedish PoliceDirección de correo verificada de foi.se
Lisa KaatiStockholm UniversityDirección de correo verificada de dsv.su.se
Daniel JönssonLinköping UniversityDirección de correo verificada de liu.se
Jonas UngerProfessor, division for Media and Information Technology, Linköping University, SwedenDirección de correo verificada de liu.se
robert kohnProfessor of Economics University of New South WalesDirección de correo verificada de unsw.edu.au

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Johan Dahlin

Kotte Consulting AB, Stockholm, Sweden

Dirección de correo verificada de johandahlin.com

Bayesian Learning Computational Statistics Machine Learning Monte Carlo Methods System Identification


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
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	106	2015
Particle Metropolis–Hastings using gradient and Hessian information J Dahlin, F Lindsten, TB Schön Statistics and computing 25, 81-92, 2015	57	2015
Ensemble approaches for improving community detection methods J Dahlin, P Svenson arXiv preprint arXiv:1309.0242, 2013	40	2013
Accelerating pseudo-marginal Metropolis-Hastings by correlating auxiliary variables J Dahlin, F Lindsten, J Kronander, TB Schön arXiv preprint arXiv:1511.05483, 2015	35	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	25	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
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	15	2018
Getting started with particle Metropolis-Hastings for inference in nonlinear dynamical models J Dahlin, TB Schön arXiv preprint arXiv:1511.01707, 2015	15	2015
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
Sparse Bayesian ARX models with flexible noise distributions J Dahlin, A Wills, B Ninness IFAC-PapersOnLine 51 (15), 25-30, 2018	14	2018
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

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