Markov chain Monte Carlo (MCMC) method for parameter estimation of nonlinear dynamical systems

M. Javvad Ur Rehman, Sarat Chandra Dass, Vijanth Sagayan Asirvadam

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

This manuscript is concerned with parameter estimation of nonlinear dynamical system. Bayesian framework is very useful for parameter estimation, Metropolis-Hastings (MH) algorithm is proposed for constructing the posterior density, which is main working procedure of Bayesian analysis. Extended Kalman Filter (EKF) gives better results in non-linear environment at each time step in which Taylor series approximation for nonlinear system is used. A performance comparison of EKF in linear and non-linear environment is proposed. This study will give us the solution for nonlinear systems, numerical integration of complex integrals and parameter estimation of stochastic differential equations (SDE).

Original languageEnglish
Title of host publicationIEEE 2015 International Conference on Signal and Image Processing Applications, ICSIPA 2015 - Proceedings
PublisherIEEE
Pages7-10
Number of pages4
ISBN (Electronic)9781479989966
DOIs
Publication statusPublished - 25 Feb 2016
Event4th IEEE International Conference on Signal and Image Processing Applications 2015 - Kuala Lumpur, Malaysia
Duration: 19 Oct 201521 Oct 2015

Conference

Conference4th IEEE International Conference on Signal and Image Processing Applications 2015
Abbreviated titleICSIPA 2015
CountryMalaysia
CityKuala Lumpur
Period19/10/1521/10/15

Keywords

  • Bayesian
  • EKF
  • MH
  • Parameter
  • SDE

ASJC Scopus subject areas

  • Computer Science Applications
  • Signal Processing

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  • Cite this

    Ur Rehman, M. J., Dass, S. C., & Asirvadam, V. S. (2016). Markov chain Monte Carlo (MCMC) method for parameter estimation of nonlinear dynamical systems. In IEEE 2015 International Conference on Signal and Image Processing Applications, ICSIPA 2015 - Proceedings (pp. 7-10). IEEE. https://doi.org/10.1109/ICSIPA.2015.7412154