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Bayesian estimation of polynomial moving average models with unknown degree of nonlinearity

Karakus, O., Kuruoglu, E. E. and Altinkaya, M. A. 2016. Bayesian estimation of polynomial moving average models with unknown degree of nonlinearity. Presented at: 24th European Signal Processing Conference (EUSIPCO), Budapest, Hungary, 29 Aug-2 Sept 2016. 24th European Signal Processing Conference (EUSIPCO). pp. 1543-1547. 10.1109/EUSIPCO.2016.7760507

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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.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
ISBN: 9780992862657
ISSN: 2219-5491
Last Modified: 12 Jan 2022 12:30

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