F
Fredrik Lindsten
Researcher at Linköping University
Publications - 132
Citations - 3077
Fredrik Lindsten is an academic researcher from Linköping University. The author has contributed to research in topics: Particle filter & Markov chain Monte Carlo. The author has an hindex of 30, co-authored 120 publications receiving 2601 citations. Previous affiliations of Fredrik Lindsten include Uppsala University & University of Cambridge.
Papers
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Proceedings Article
Sequential Kernel Herding: Frank-Wolfe Optimization for Particle Filtering
TL;DR: Experiments indicate an improvement of accuracy over random and quasi-Monte Carlo sampling on a robot localization task and the additional computational cost to generate the particles through optimization can be justified.
Rao-Blackwellised particle methods for inference and identification
TL;DR: This work considers the two related problems of state inference in nonlinear dynamical systems and nonlinear system identification based on noisy observations from some (in general) nonlin systems.
Journal ArticleDOI
Sequential Monte Carlo Methods for System Identification
Thomas B. Schön,Fredrik Lindsten,Johan Dahlin,Johan Wågberg,Christian A. Naesseth,Andreas Svensson,Liang Dai +6 more
TL;DR: In this article, the authors describe two general strategies for creating such combinations and discuss why SMC is a natural tool for implementing these strategies, and why the particle filter can be used to implement these strategies.
Posted Content
Accelerating pseudo-marginal Metropolis-Hastings by correlating auxiliary variables
TL;DR: A modication to the pmMH algorithm is proposed in which a Crank-Nicolson (CN) proposal is used instead, which results in that a positive correlation in the auxiliary variables is introduced.
Posted Content
Nested Sequential Monte Carlo Methods
TL;DR: Nested sequential Monte Carlo (NSMC) as discussed by the authors generalizes the SMC framework by requiring only approximate, properly weighted, samples from the proposal distribution, while still resulting in a correct SMC algorithm.