Pseudo-Marginal MCMC for Parameter Estimation in α-Stable Distributions**The first author acknowledges partial funding from EPSRC-DTG- 2013/EU, while the second and third authors were supported by the EPSRC BTaRoT project EP/K020153/1; the third author was also funded by the Learning of complex dynamical systems project (Contract number: 637-2014-466) from the Swedish Research Council.

The Third ◽

Project Contract

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 ◽

Sequential Monte Carlo Methods ◽

Swedish Research Council

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 ◽

Author(s):

Johan Dahlin ◽

Fredrik Lindsten ◽

Thomas B. Schön

Keyword(s):

Dynamical Systems ◽

Research Council ◽

Probabilistic Modelling ◽

Mail Address ◽

Quasi Newton ◽

E Mail

On The Validity of Decomposition for Distributed Parameter Estimation in Complex Dynamical Systems: The Case of Cooling Systems

2020 52nd North American Power Symposium (NAPS) ◽

10.1109/naps50074.2021.9449723 ◽

2021 ◽

Author(s):

Dan Wu ◽

Janak Agrawal ◽

Pallavi Bharadwaj ◽

Le Li ◽

Jinyi Zhang ◽

...

Keyword(s):

Parameter Estimation ◽

Dynamical Systems ◽

Distributed Parameter ◽

Cooling Systems ◽

Distributed Parameter Estimation

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).

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2015.12.257 ◽

2015 ◽

Vol 48 (28) ◽

pp. 975-980 ◽

Author(s):

Andreas Svensson ◽

Thomas B. Schön ◽

Manon Kok

Keyword(s):

Dynamical Systems ◽

Particle Filter ◽

State Space ◽

Research Council ◽

Probabilistic Modelling ◽

Nonlinear State

Bayesian optimal experimental design for parameter estimation and response predictions in complex dynamical systems

Procedia Engineering ◽

10.1016/j.proeng.2017.09.205 ◽

2017 ◽

Vol 199 ◽

pp. 972-977 ◽

Author(s):

Costas Papadimitriou ◽

Costas Argyris

Keyword(s):

Parameter Estimation ◽

Dynamical Systems ◽

Experimental Design ◽

Optimal Experimental Design ◽

Complex Dynamical Systems

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

Probabilistic scheme for joint parameter estimation and state prediction in complex dynamical systems

Physical Review E ◽

10.1103/physreve.98.063305 ◽

2018 ◽

Vol 98 (6) ◽

Author(s):

Sara Pérez-Vieites ◽

Inés P. Mariño ◽

Joaquín Míguez

Keyword(s):

Parameter Estimation ◽

Dynamical Systems ◽

State Prediction

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

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

Bayesian nonparametric identification of piecewise affine ARX systems**This work was supported by the projects: Learning of complex dy-namical 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.2015.12.213 ◽

2015 ◽

Vol 48 (28) ◽

pp. 709-714 ◽

Author(s):

Johan Wågberg ◽

Fredrik Lindsten ◽

Thomas B. Schön

Keyword(s):

Dynamical Systems ◽

Research Council ◽

Probabilistic Modeling ◽

Nonparametric Identification ◽

Bayesian Nonparametric ◽

Piecewise Affine ◽

Swedish Research Council