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Title: Bayesian estimation of polynomial moving average models with unknown degree of nonlinearity
Authors: Karakuş, Oktay
Kuruoğlu, Ercan Engin
Altınkaya, Mustafa Aziz
Keywords: Nonlinearity degree estimation
Reversible jump MCMC
Nonlinear optics
Markov processes
Monte Carlo method
Publisher: IEEE
Source: Karakuş, O., Kuruoğlu, E. E., and Altınkaya, M. A. (2016, August 28 - September 2). Bayesian estimation of polynomial moving average models with unknown degree of nonlinearity. Paper presented at the 24th European Signal Processing Conference, EUSIPCO 2016. doi:10.1109/EUSIPCO.2016.7760507
Abstract: Various real world phenomena such as optical communication channels, power amplifiers and movement of sea vessels exhibit nonlinear characteristics. The nonlinearity degree of such systems is assumed to be known as a general intention. In this paper, we contribute to the literature with a Bayesian estimation method based on reversible jump Markov chain Monte Carlo (RJMCMC) for polynomial moving average (PMA) models. Our use of RJMCMC is novel and unique in the way of estimating both model memory and the nonlinearity degree. This offers greater flexibility to characterize the models which reflect different nonlinear characters of the measured data. In this study, we aim to demonstrate the potentials of RJMCMC in the identification for PMA models due to its potential of exploring nonlinear spaces of different degrees by sampling.
Description: 24th European Signal Processing Conference, EUSIPCO 2016; Hotel Hilton BudapestBudapest; Hungary; 28 August 2016 through 2 September 2016
ISBN: 9780992862657
ISSN: 2219-5491
Appears in Collections:Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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