Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Estimation of the nonlinearity degree for polynomial autoregressive processes with RJMCMC

Karakus, Oktay, Kuruoglu, Ercan E. and Altinkaya, Mustafa A. 2015. Estimation of the nonlinearity degree for polynomial autoregressive processes with RJMCMC. Presented at: 2015 European Signal Processing Conference (EUSIPCO 2015), 31 August-4 September 2015. 2015 23rd European Signal Processing Conference (EUSIPCO). IEEE, pp. 958-962. 10.1109/eusipco.2015.7362524

Full text not available from this repository.


Despite the popularity of linear process models in signal and image processing, various real life phenomena exhibit nonlinear characteristics. Compromising between the realistic and computationally heavy nonlinear models and the simplicity of linear estimation methods, linear in the parameters nonlinear models such as polynomial autoregressive (PAR) models have been accessible analytical tools for modelling such phenomena. In this work, we aim to demonstrate the potentials of Reversible Jump Markov Chain Monte Carlo (RJMCMC) which is a successful statistical tool in model dimension estimation in nonlinear process identification. We explore the capability of RJMCMC in jumping not only between spaces with different dimensions, but also between different classes of models. In particular, we demonstrate the success of RJMCMC in sampling in linear and nonlinear spaces of varying dimensions for the estimation of PAR processes.

Item Type: Conference or Workshop Item (Paper)
Date Type: Published Online
Status: Published
Schools: Computer Science & Informatics
Publisher: IEEE
ISBN: 9780992862633
Last Modified: 07 Feb 2022 11:15

Citation Data

Cited 5 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item