Nonlinear State Space Smoothing Using the Conditional Particle Filter**This work was supported by the project Probabilistic modelling of dynamical systems (Contract number: 621-2013-5524) and CADICS, a Linnaeus Center, both funded by the Swedish Research Council (VR).

Quasi-Newton particle Metropolis-Hastings**E-mail address to corresponding author: [email protected]. This work was supported by: Learning of complex dynamical systems (Contract num. 637-2014-466), Probabilistic modelling of dynamical systems (Contract num. 621-2013-5524) and CADICS, a Linnaeus Center, all funded by the Swedish Research Council.

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2015.12.258 ◽

2015 ◽

Vol 48 (28) ◽

pp. 981-986 ◽

Cited By ~ 1

Author(s):

Johan Dahlin ◽

Fredrik Lindsten ◽

Thomas B. Schön

Keyword(s):

Dynamical Systems ◽

Research Council ◽

Probabilistic Modelling ◽

Complex Dynamical Systems ◽

Mail Address ◽

Quasi Newton ◽

E Mail

Predictive Control of Discrete Time Stochastic Nonlinear State Space Dynamical Systems: A Particle Nonparametric Approach

Applied Mathematics & Optimization ◽

10.1007/s00245-017-9462-9 ◽

2017 ◽

Vol 80 (1) ◽

pp. 165-194

Author(s):

Jean-Pierre Vila ◽

Jean-Pierre Gauchi

Keyword(s):

Dynamical Systems ◽

State Space ◽

Predictive Control ◽

Discrete Time ◽

Nonparametric Approach ◽

A kernel density-based particle filter for state and time-varying parameter estimation in nonlinear state-space models

2017 25th European Signal Processing Conference (EUSIPCO) ◽

10.23919/eusipco.2017.8081492 ◽

2017 ◽

Cited By ~ 2

Author(s):

Cheng Cheng ◽

Jean-Yves Tourneret

Keyword(s):

Parameter Estimation ◽

Particle Filter ◽

State Space ◽

Kernel Density ◽

State Space Models ◽

Time Varying ◽

Time Varying Parameter ◽

The Marginal Rao-Blackwellized Particle Filter for Mixed Linear/Nonlinear State Space Models

Chinese Journal of Aeronautics ◽

10.1016/s1000-9361(07)60054-5 ◽

2007 ◽

Vol 20 (4) ◽

pp. 346-352 ◽

Cited By ~ 13

Author(s):

Jianjun Yin ◽

Jianqiu Zhang ◽

Klaas Mike

Keyword(s):

Particle Filter ◽

State Space ◽

State Space Models ◽

Sequential Monte Carlo Methods for System Identification**This work was supported by the projects Learning of complex dynamical systems (Contract number: 637-2014-466) and Probabilistic modeling of dynamical systems (Contract number: 621-2013-5524), both funded by the Swedish Research Council.

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2015.12.224 ◽

2015 ◽

Vol 48 (28) ◽

pp. 775-786 ◽

Cited By ~ 9

Author(s):

Thomas B. Schön ◽

Fredrik Lindsten ◽

Johan Dahlin ◽

Johan Wågberg ◽

Christian A. Naesseth ◽

...

Keyword(s):

Monte Carlo ◽

System Identification ◽

Dynamical Systems ◽

Monte Carlo Methods ◽

Research Council ◽

Sequential Monte Carlo ◽

Probabilistic Modeling ◽

Complex Dynamical Systems ◽

Sequential Monte Carlo Methods ◽

Swedish Research Council

On Identification via EM with Latent Disturbances and Lagrangian Relaxation**This work was supported by the Australian Research Council (DP130100551), and the Swedish Research Council (VR) as part of the project: Probabilistic modeling of dynamical systems (Contract number: 621-2013-5524).

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2015.12.102 ◽

2015 ◽

Vol 48 (28) ◽

pp. 69-74

Author(s):

Jack Umenberger ◽

Johan Wågberg ◽

Ian R. Manchester ◽

Thomas B. Schön

Keyword(s):

Dynamical Systems ◽

Lagrangian Relaxation ◽

Research Council ◽

Probabilistic Modeling ◽

Australian Research Council ◽

Swedish Research Council

Online sparse Gaussian process regression using FITC and PITC approximations**This research is supported by the Dutch Technology Foundation STW, which is part of the Netherlands Organisation for Scientific Research (NWO), and which is partly funded by the Ministry of Economic Affairs. The work was also supported by the Swedish research Council (VR) via the project Probabilistic modeling of dynamical systems (Contract number: 621-2013-5524).

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2015.12.212 ◽

2015 ◽

Vol 48 (28) ◽

pp. 703-708 ◽

Cited By ~ 8

Author(s):

Hildo Bijl ◽

Jan-Willem van Wingerden ◽

Thomas B. Schön ◽

Michel Verhaegen

Keyword(s):

Dynamical Systems ◽

The Netherlands ◽

Gaussian Process ◽

Research Council ◽

Scientific Research ◽

Gaussian Process Regression ◽

Probabilistic Modeling ◽

Technology Foundation

Learning Nonlinear State-Space Models Using Smooth Particle-Filter-Based Likelihood Approximations

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2018.09.216 ◽

2018 ◽

Vol 51 (15) ◽

pp. 652-657

Author(s):

Andreas Svensson ◽

Fredrik Lindsten ◽

Thomas B. Schön

Keyword(s):

Particle Filter ◽

State Space ◽

State Space Models ◽

Particle filtering based identification for autonomous nonlinear ODE models**This work was supported by the project Probabilistic modeling of dynamical systems (Contract number: 621-2013-5524) funded by the Swedish Research Council. (*) are members of the LCCC Linnaeus Center and the ELLIIT Excellence Center at Lund University

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2015.12.163 ◽

2015 ◽

Vol 48 (28) ◽

pp. 415-420

Author(s):

Jerker Nordh ◽

Torbjörn Wigren ◽

Thomas B. Schön ◽

Bo Bernhardsson

Keyword(s):

Dynamical Systems ◽

Particle Filtering ◽

Research Council ◽

Probabilistic Modeling ◽

Ode Models ◽

Nonlinear Ode

Newton-based maximum likelihood estimation in nonlinear state space models**E-mail address to corresponding author: [email protected]. This work is supported by CADICS, a Linnaeus Center, and by the project Probabilistic modeling of dynamical systems (Contract number: 621-2013-5524), both funded by the Swedish Research Council (VR).

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2015.12.160 ◽

2015 ◽

Vol 48 (28) ◽

pp. 398-403 ◽

Cited By ~ 3

Author(s):

Manon Kok ◽

Johan Dahlin ◽

Thomas B. Schön ◽

Adrian Wills

Keyword(s):

Maximum Likelihood ◽

Dynamical Systems ◽

Maximum Likelihood Estimation ◽

Research Council ◽

Likelihood Estimation ◽

State Space Models ◽

Mail Address ◽

E Mail ◽