Recovering sinusoids from noisy data using bayesian inference with simulated annealing
Mathematical and Computational Applications, vol.16, no.2, pp.382-391, 2011 (Scopus, TRDizin)
- Publication Type: Article / Article
- Volume: 16 Issue: 2
- Publication Date: 2011
- Doi Number: 10.3390/mca16020382
- Journal Name: Mathematical and Computational Applications
- Journal Indexes: Scopus, TR DİZİN (ULAKBİM)
- Page Numbers: pp.382-391
- Keywords: Bayesian Statistical Inference Simulated Annealing, Cramér-Rao lower bound, Parameter estimations, Power Spectral Density
- Istanbul University Affiliated: No
Abstract
In this paper, we studied Bayesian analysis proposed by Bretthorst[6] for a general signal model equation and combined it with a simulated annealing (SA) algorithm to obtain a global maximum of a posterior probability density function (PDF) for frequencies. Thus, this analysis offers different approach to finding parameter values through a directed, but random, search of the parameter space. For this purpose, we developed a Mathematica code of this Bayesian approach together with SA and used it for recovering sinusoids from noisy data. Simulations results support its effectiveness. Copyright © Association for Scientific Research.