A Bayesian parameter learning procedure for nonlinear dynamical systems via the ensemble Kalman filter

Muhammad Javvad Ur Rehman, Sarat C. Dass, Vijanth S. Asirvadam

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

1 Citation (Scopus)

Abstract

Dynamical systems are a natural and convenient way to model the evolution of processes observed in practice. When uncertainty is considered and incorporated, these system become known as stochastic dynamical systems. Based on observations made from stochastic dynamical systems, we consider the issue of parameter learning, and a related state estimation problem. We develop a Markov Chain Monte Carlo (MCMC) algorithm, which is an iterative method, for parameter inference. Within the parameter learning steps, the MCMC algorithm requires to perform state estimation for which the target distribution is constructed by using the Ensemble Kalman filter (EnKF). The methodology is illustrated using two examples of nonlinear stochastic dynamical systems.

Original languageEnglish
Title of host publication2018 IEEE 14th International Colloquium on Signal Processing & Its Applications (CSPA)
PublisherIEEE
Pages161-166
Number of pages6
ISBN (Electronic)9781538603895
DOIs
Publication statusPublished - 31 May 2018
Event14th IEEE International Colloquium on Signal Processing and its Application 2018 - Batu Feringghi, Penang, Malaysia
Duration: 9 Mar 201810 Mar 2018

Conference

Conference14th IEEE International Colloquium on Signal Processing and its Application 2018
Abbreviated titleCSPA 2018
CountryMalaysia
CityPenang
Period9/03/1810/03/18

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Signal Processing

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    Rehman, M. J. U., Dass, S. C., & Asirvadam, V. S. (2018). A Bayesian parameter learning procedure for nonlinear dynamical systems via the ensemble Kalman filter. In 2018 IEEE 14th International Colloquium on Signal Processing & Its Applications (CSPA) (pp. 161-166). IEEE. https://doi.org/10.1109/CSPA.2018.8368705