Topics
Applied Physics: Lasers and Accelerators, Condensed Matter, Soft matter and materials science
plenary speech
Prof. Dursun Üstündağ
Marmara University, Istanbul, Turkey
Title: An Efficient Method for Extracting Sinusoids From Noisy Data
Abstract: This paper proposes a novel algorithm that combines a global nonlinear optimization routine based on the Bayesian Inference Reversible Jump Markov Chain Monte Carlo (BI-RJMCMC) method with a range of proposal distributions and simulated annealing (SA). This approach enables a global exploration of the parameters and number of parameters, circumventing the issue of local minima and facilitating a convergence to the modes of the full posterior distribution through an efficient process. The estimation of the parameters can be deduced from these probabilities along with the corresponding variances. Additionally, in instances where the variance of the noise is not known, an estimate of the noise variance can be obtained. A comparison of the results with those obtained using alternative methods, including Bretthorst, Dou, and Hodgson's methods, has been undertaken. The approach outlined in this paper demonstrates several advantages.
Bio: Dursun Ustundag was graduated from the Technical university of Istanbul as a mathematical engineering. During his undergraduate studies, he was supported financially by Turkish Scientific research Council (TUBITAK). After he was graduated from the university, he got a grant from Turkish Ministry of Education to do PhD study in mathematics in Great Britain. He had stayed there since he was awarded a Ph.D. degree from the University of Birmingham, where he investigated image reconstruction methods based on maximum entropy principle and applied them to data taken from X-ray telescopes and positron emission camera. He has been in Marmara University as a member of staff since 1994 in the department of Mathematics as a member of the applied mathematics group.
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