TY  - CONF
N2  - 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. © 2018 IEEE.
N1  - cited By 2; Conference of 14th IEEE International Colloquium on Signal Processing and its Application, CSPA 2018 ; Conference Date: 9 March 2018 Through 10 March 2018; Conference Code:136804
ID  - scholars10302
TI  - A Bayesian parameter learning procedure for nonlinear dynamical systems via the ensemble Kalman filter
SP  - 161
KW  - Bandpass filters; Dynamical systems; Inference engines; Iterative methods; Kalman filters; Markov processes; Monte Carlo methods; Nonlinear dynamical systems; State estimation; Stochastic systems
KW  -  Bayesian; Ensemble Kalman Filter; Estimation problem; Markov chain monte carlo algorithms; MCMC algorithms; Parameter inference; Parameter learning; Stochastic dynamical system
KW  -  Parameter estimation
AV  - none
A1  - Ur Rehman, M.J.
A1  - Dass, S.C.
A1  - Asirvadam, V.S.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048821948&doi=10.1109%2fCSPA.2018.8368705&partnerID=40&md5=1caa383c4f3384dad1ddf41d7ba31126
EP  - 166
Y1  - 2018///
PB  - Institute of Electrical and Electronics Engineers Inc.
SN  - 9781538603895
ER  -