2016
Conference article  Open Access

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

Karakus O., Kuruoglu E. E., Altinkaya M.

Monte Carlo method  Bayesian estimation  Nonlinear stochastic process  Reversible jump  Model selection  Markov processes  Markov chain Monte Carlo (RJMCMC)  Nonlinearity degree estimation  Polynomials  Reversible jump MCMC  Nonlinear optics  Polynomial moving average 

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 nonlin- earity 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.

Source: EUSIPCO 2016 - European Signal Processing Conference, pp. 1543–1547, Budapest, Ungaria, 29 August - 02 September 2016


Metrics



Back to previous page
BibTeX entry
@inproceedings{oai:it.cnr:prodotti:359254,
	title = {Bayesian estimation of polynomial moving average models with unknown degree of nonlinearity},
	author = {Karakus O. and Kuruoglu E.  E. and Altinkaya M.},
	doi = {10.1109/eusipco.2016.7760507},
	booktitle = {EUSIPCO 2016 - European Signal Processing Conference, pp. 1543–1547, Budapest, Ungaria, 29 August - 02 September 2016},
	year = {2016}
}