Detail publikace

A particle stochastic approximation EM algorithm to identify jump Markov nonlinear models

PAPEŽ, M.

Originální název

A particle stochastic approximation EM algorithm to identify jump Markov nonlinear models

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

The identification of static parameters in jump Markov nonlinear models (JMNMs) poses a key challenge in explaining nonlinear and abruptly changing behavior of dynamical systems. This paper introduces a stochastic approximation expectation maximization algorithm to facilitate offline maximum likelihood parameter estimation in JMNMs. The method relies on the construction of a particle Gibbs kernel that takes advantage of the inherent structure of the model to increase the efficiency through Rao-Blackwellization. Numerical examples illustrate that the proposed solution outperforms related approaches.

Klíčová slova

Sequential Monte Carlo; particle Markov chain Monte Carlo; particle Gibbs with ancestor sampling; stochastic approximation; expectation maximization; jump Markov nonlinear models; Rao-Blackwellization

Autoři

PAPEŽ, M.

Vydáno

9. 7. 2018

Nakladatel

International Federation of Automatic Control (IFAC)

ISSN

2405-8963

Periodikum

IFAC-PapersOnLine (ELSEVIER)

Ročník

51

Číslo

15

Stát

Nizozemsko

Strany od

676

Strany do

681

Strany počet

6

BibTex

@inproceedings{BUT148841,
  author="Milan {Papež}",
  title="A particle stochastic approximation EM algorithm to identify jump Markov nonlinear models",
  booktitle="Proceedings of the 18th Symposium on System Identification, SYSID 2018",
  year="2018",
  journal="IFAC-PapersOnLine (ELSEVIER)",
  volume="51",
  number="15",
  pages="676--681",
  publisher="International Federation of Automatic Control (IFAC)",
  doi="10.1016/j.ifacol.2018.09.205",
  issn="2405-8963"
}