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dc.contributor.authorUstundag, D.
dc.contributor.authorCevri, M.
dc.date.accessioned2021-03-03T19:30:22Z
dc.date.available2021-03-03T19:30:22Z
dc.identifier.citationCevri M., Ustundag D., "Performance Evaluation of Gibbs Sampling for Bayesian Extracting Sinusoids", COMPUTATIONAL PROBLEMS IN ENGINEERING, cilt.307, ss.13-31, 2014
dc.identifier.issn1876-1100
dc.identifier.othervv_1032021
dc.identifier.otherav_54e79541-86da-4d63-bef2-4aab27d35b18
dc.identifier.urihttp://hdl.handle.net/20.500.12627/60066
dc.identifier.urihttps://doi.org/10.1007/978-3-319-03967-1_2
dc.description.abstractThis chapter involves problems of estimating parameters of sinusoids from white noisy data by using Gibbs sampling (GS) in a Bayesian inferential framework which allows us to incorporate prior knowledge about the nature of sinusoidal data into the model. Modifications of its algorithm is tested on data generated from synthetic signals and its performance is compared with conventional estimators such as Maximum Likelihood (ML) and Discrete Fourier Transform (DFT) under a variety of signal to noise ratio (SNR) conditions and different lengths of data sampling (N), regarding to Cramer-Rao lower bound (CRLB) that is a limit on the best possible performance achievable by an unbiased estimator given a dataset. All simulation results show its effectiveness in frequency and amplitude estimation of noisy sinusoids.
dc.language.isoeng
dc.subjectMatematik
dc.subjectFizik
dc.subjectBilgisayar Bilimleri
dc.subjectGenel Fizik
dc.subjectTemel Bilimler
dc.subjectFİZİK, MATEMATİK
dc.subjectTemel Bilimler (SCI)
dc.subjectMATEMATİK, UYGULAMALI
dc.titlePerformance Evaluation of Gibbs Sampling for Bayesian Extracting Sinusoids
dc.typeMakale
dc.relation.journalCOMPUTATIONAL PROBLEMS IN ENGINEERING
dc.contributor.departmentMarmara Üniversitesi , ,
dc.identifier.volume307
dc.identifier.startpage13
dc.identifier.endpage31
dc.contributor.firstauthorID212260


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