Istituto di Scienza e Tecnologie dell'Informazione     
Karakus O., Kuruoglu E. E., Altinkaya M. A. Bayesian Volterra system identification using reversible jump MCMC algorithm. In: Signal Processing, vol. 141 (December 2017) pp. 125 - 136. Elsevier, 2017.
Volterra systems have had significant success in modelling nonlinear systems in various real-world applications. However, it is generally assumed that the nonlinearity degree of the system is known beforehand. In this paper, we contribute to the literature on Volterra system identification (VSI) with a numerical Bayesian approach which identifies model coefficients and the nonlinearity degree concurrently. Although this numerical Bayesian method, namely reversible jump Markov chain Monte Carlo (RJMCMC) algorithm has been used with success in various model selection problems, our use is in a novel context in the sense that both memory size and nonlinearity degree are estimated. The aforementioned study ensures an anomalous approach to RJMCMC and provides a new understanding on its flexible use which enables trans-structural transitions between different classes of models in addition to transdimensional transitions for which it is classically used. We study the performance of the method on synthetically generated data including OFDM communications over a nonlinear channel.
URL: http://www.sciencedirect.com/science/article/pii/S0165168417302025
DOI: 10.1016/j.sigpro.2017.05.031
Subject Reversible jump Markov chain Monte Carlo (RJMCMC) algorithm
Bayesian model selection
Trans-class sampling
Volterra systems
Nonlinear system identification
G.3 PROBABILITY AND STATISTICS. Probabilistic algorithms (including Monte Carlo)
91G60 Numerical methods (including Monte Carlo methods)
62C10 Bayesian problems; characterization of Bayes procedures

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