Identification of Gaussian process state-space models with particle stochastic approximation EM

IFAC Proceedings Volumes (IFAC-PapersOnline)

Volumn 19, Issue , 2014, Pages 4097-4102

  Frigola, Roger ^a   Lindsten, Fredrik ^b   Schön, Thomas B ^b,c   Rasmussen, Carl E ^a  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^b LINKÖPING UNIVERSITY   (Sweden)

^c UPPSALA UNIVERSITY   (Sweden)

Author keywords

Bayesian; Gaussian processes; Non parametric identification; System identification

Indexed keywords

APPROXIMATION ALGORITHMS; APPROXIMATION THEORY; AUTOMATION; DYNAMICAL SYSTEMS; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); IDENTIFICATION (CONTROL SYSTEMS); MARKOV PROCESSES; MAXIMUM LIKELIHOOD; MONTE CARLO METHODS; NONLINEAR DYNAMICAL SYSTEMS; STATE SPACE METHODS; STOCHASTIC CONTROL SYSTEMS; STOCHASTIC MODELS; STOCHASTIC SYSTEMS;

BAYESIAN; GAUSSIAN PROCESSES; IDENTIFICATION OF THE PARAMETERS; NON-PARAMETRIC IDENTIFICATION; NONPARAMETRIC REPRESENTATION; PARTICLE MARKOV CHAIN MONTE CARLO; STOCHASTIC APPROXIMATIONS; SYSTEM DYNAMICS;

PARAMETER ESTIMATION;

EID: 84929773403     PISSN: 14746670     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.3182/20140824-6-za-1003.01843     Document Type: Conference Paper

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