@inproceedings{scholars7183,
           pages = {7--10},
           title = {Markov chain Monte Carlo (MCMC) method for parameter estimation of nonlinear dynamical systems},
         journal = {IEEE 2015 International Conference on Signal and Image Processing Applications, ICSIPA 2015 - Proceedings},
       publisher = {Institute of Electrical and Electronics Engineers Inc.},
             doi = {10.1109/ICSIPA.2015.7412154},
            year = {2016},
            note = {cited By 2; Conference of 4th IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2015 ; Conference Date: 19 October 2015 Through 21 October 2015; Conference Code:119504},
        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). {\^A}{\copyright} 2015 IEEE.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84971644702&doi=10.1109\%2fICSIPA.2015.7412154&partnerID=40&md5=5fbb87ef138e915bb6c4253b8846dc88},
        keywords = {Differential equations; Dynamical systems; Extended Kalman filters; Image processing; Markov processes; Monte Carlo methods; Nonlinear analysis; Nonlinear dynamical systems; Nonlinear equations; Nonlinear systems; Numerical methods; Stochastic systems, Bayesian; Bayesian frameworks; Markov chain Monte Carlo method; Numerical integrations; Parameter; Performance comparison; Stochastic differential equations; Taylor series approximation, Parameter estimation},
            isbn = {9781479989966},
          author = {Ur Rehman, M. J. and Dass, S. C. and Asirvadam, V. S.}
}