Performance Analysis of Gibbs Sampling for Bayesian Extracting Sinusoids

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Atıf İçin Kopyala

Cevri M., Üstündag D.

INTERNATIONAL JOURNAL OF MATHEMATICAL MODELS AND METHODS IN APPLIED SCIENCES, sa.15, ss.148-154, 2021 (Hakemli Dergi)

• Yayın Türü: Makale / Tam Makale
• Basım Tarihi: 2021
• Doi Numarası: 10.46300/9101.2021.15.19
• Dergi Adı: INTERNATIONAL JOURNAL OF MATHEMATICAL MODELS AND METHODS IN APPLIED SCIENCES
• Derginin Tarandığı İndeksler: EBSCO Education Source, Index Chemicus (IC), Index Copernicus
• Sayfa Sayıları: ss.148-154
• İstanbul Üniversitesi Adresli: Evet

Özet

This paper involves problems of estimating parameters of sinusoids from white noisy data by using Gibbs sampling (GS) in a Bayesian framework. 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) and different length of data sampling (N), regarding to Cramér-Rao lower bound (CRLB). All simulation results show its effectiveness in frequency and amplitude estimation of sinusoids. 

Keywords—Bayesian inference; parameter estimation; Gibbs sampling; Cramér-Rao lower bound; power spectral density.