Particle filters and Markov chains for learning of dynamical systems

Particle filters and Markov chains for learning of dynamical systems

Sequential Monte Carlo (SMC) and Markov chain Monte Carlo (MCMC) methods provide computational tools for systematic inference and learning in complex dynamical systems, such as nonlinear and non-Gaussian state-space models. This thesis builds upon several methodological advances within these classes...

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Bibliographic Details
Main Author: Lindsten, Fredrik
Format: Doctoral Thesis
Language:English
Published: Linköpings universitet, Reglerteknik 2013
Subjects:
Bayesian learning
System identification
Sequential Monte Carlo
Markov chain Monte Carlo
Particle MCMC
Particle filters
Particle smoothers
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-97692
http://nbn-resolving.de/urn:isbn:978-91-7519-559-9 (print)

Internet

http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-97692
http://nbn-resolving.de/urn:isbn:978-91-7519-559-9 (print)

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