Estimating parameters of sinusoids from noisy data using Bayesian inference with simulated annealing

Authors:
Dursun Üstündang;Mehmet Cevri
Affiliations:
The Department of Mathematics, The Faculty of Science and Letters, The University of Marmara, Istanbul, Turkey;The Department of Mathematics, The Faculty of Science and Letters, The University of Marmara, Istanbul, Turkey
Venue:
WSEAS Transactions on Signal Processing
Year:
2008

Citing 8
Cited 3

Minimizing multimodal functions of continuous variables with the “simulated annealing” algorithm: Corrigenda for this article is available here

ACM Transactions on Mathematical Software (TOMS)
Numerical recipes in C: the art of scientific computing

Numerical recipes in C: the art of scientific computing
Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing

Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing
`` Direct Search'' Solution of Numerical and Statistical Problems

Journal of the ACM (JACM)
Statistical Digital Signal Processing and Modeling

Statistical Digital Signal Processing and Modeling
Bayesian Logical Data Analysis for the Physical Sciences

Bayesian Logical Data Analysis for the Physical Sciences
An Introduction to Programming with Mathematica, Third Edition

An Introduction to Programming with Mathematica, Third Edition
Extended Kalman filter for parameter estimation of wideband polynomial phase signals in sensors arrays

SIP'08 Proceedings of the 7th WSEAS International Conference on Signal Processing

Joint detection and estimation of noisy sinusoids using Bayesian inference with reversible jump MCMC algorithm

WAV'09 Proceedings of the 3rd WSEAS international symposium on Wavelets theory and applications in applied mathematics, signal processing & modern science
Bayesian estimation of sinusoidal signals via parallel tempering

WAV'09 Proceedings of the 3rd WSEAS international symposium on Wavelets theory and applications in applied mathematics, signal processing & modern science
A novel well conditions analysis method through SFPI algorithm

WSEAS Transactions on Systems and Control

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we consider Bayesian analysis proposed by Bretthorst for estimating parameters of the corrupted signals and incorporate it with a simulated annealing algorithm to obtain a global maximum of the posterior probability density of the parameters. Thus, this analysis offers different approach to find parameter values through a directed, but random, search of the parameter space. For this purpose, we developed a Mathematica code of this Bayesian approach and used it for recovering sinusoids corrupted by random noise. The simulation results support the effectiveness of the method.