Recovering Sinusoids from Noisy Data Using Bayesian Inference with Simulated Annealing
The Department of Mathematics, Faculty of Science and Arts Marmara University, 34722, Kadıköy, Istanbul, Turkey
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Math. Comput. Appl. 2011, 16(2), 382-391; https://doi.org/10.3390/mca16020382
Published: 1 August 2011
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.
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MDPI and ACS Style
Üstündağ, D.; Cevri, M. Recovering Sinusoids from Noisy Data Using Bayesian Inference with Simulated Annealing. Math. Comput. Appl. 2011, 16, 382-391. https://doi.org/10.3390/mca16020382
AMA Style
Üstündağ D, Cevri M. Recovering Sinusoids from Noisy Data Using Bayesian Inference with Simulated Annealing. Mathematical and Computational Applications. 2011; 16(2):382-391. https://doi.org/10.3390/mca16020382
Chicago/Turabian StyleÜstündağ, Dursun, and Mehmet Cevri. 2011. "Recovering Sinusoids from Noisy Data Using Bayesian Inference with Simulated Annealing" Mathematical and Computational Applications 16, no. 2: 382-391. https://doi.org/10.3390/mca16020382
