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

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

doi:10.1109/ICSIPA.2015.7412154

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

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

^*Corresponding author for this work

School of Mathematical & Computer Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 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 language	English
Title of host publication	IEEE 2015 International Conference on Signal and Image Processing Applications, ICSIPA 2015 - Proceedings
Publisher	IEEE
Pages	7-10
Number of pages	4
ISBN (Electronic)	9781479989966
DOIs	https://doi.org/10.1109/ICSIPA.2015.7412154
Publication status	Published - 25 Feb 2016
Event	4th IEEE International Conference on Signal and Image Processing Applications 2015 - Kuala Lumpur, Malaysia Duration: 19 Oct 2015 → 21 Oct 2015

Conference

Conference	4th IEEE International Conference on Signal and Image Processing Applications 2015
Abbreviated title	ICSIPA 2015
Country/Territory	Malaysia
City	Kuala Lumpur
Period	19/10/15 → 21/10/15

Keywords

Bayesian
EKF
MH
Parameter
SDE

ASJC Scopus subject areas

Computer Science Applications
Signal Processing

Access to Document

10.1109/ICSIPA.2015.7412154

Cite this

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title = "Markov chain Monte Carlo (MCMC) method for parameter estimation of nonlinear dynamical systems",

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).",

keywords = "Bayesian, EKF, MH, Parameter, SDE",

author = "{Ur Rehman}, {M. Javvad} and Dass, {Sarat Chandra} and Asirvadam, {Vijanth Sagayan}",

year = "2016",

month = feb,

day = "25",

doi = "10.1109/ICSIPA.2015.7412154",

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booktitle = "IEEE 2015 International Conference on Signal and Image Processing Applications, ICSIPA 2015 - Proceedings",

publisher = "IEEE",

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note = "4th IEEE International Conference on Signal and Image Processing Applications 2015, ICSIPA 2015 ; Conference date: 19-10-2015 Through 21-10-2015",

}

Ur Rehman, MJ, Dass, SC & Asirvadam, VS 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. IEEE, pp. 7-10, 4th IEEE International Conference on Signal and Image Processing Applications 2015, Kuala Lumpur, Malaysia, 19/10/15. https://doi.org/10.1109/ICSIPA.2015.7412154

Markov chain Monte Carlo (MCMC) method for parameter estimation of nonlinear dynamical systems. / Ur Rehman, M. Javvad; Dass, Sarat Chandra; Asirvadam, Vijanth Sagayan.
IEEE 2015 International Conference on Signal and Image Processing Applications, ICSIPA 2015 - Proceedings. IEEE, 2016. p. 7-10.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Markov chain Monte Carlo (MCMC) method for parameter estimation of nonlinear dynamical systems

AU - Ur Rehman, M. Javvad

AU - Dass, Sarat Chandra

AU - Asirvadam, Vijanth Sagayan

PY - 2016/2/25

Y1 - 2016/2/25

N2 - 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).

AB - 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).

KW - Bayesian

KW - EKF

KW - MH

KW - Parameter

KW - SDE

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U2 - 10.1109/ICSIPA.2015.7412154

DO - 10.1109/ICSIPA.2015.7412154

M3 - Conference contribution

AN - SCOPUS:84971644702

SP - 7

EP - 10

BT - IEEE 2015 International Conference on Signal and Image Processing Applications, ICSIPA 2015 - Proceedings

PB - IEEE

T2 - 4th IEEE International Conference on Signal and Image Processing Applications 2015

Y2 - 19 October 2015 through 21 October 2015

ER -

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

Abstract

Conference

Keywords

ASJC Scopus subject areas

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