Performance Analysis of Gibbs Sampling for Bayesian Extracting Sinusoids
INTERNATIONAL JOURNAL OF MATHEMATICAL MODELS AND METHODS IN APPLIED SCIENCES, no.15, pp.148-154, 2021 (Peer-Reviewed Journal)
- Publication Type: Article / Article
- Publication Date: 2021
- Doi Number: 10.46300/9101.2021.15.19
- Journal Name: INTERNATIONAL JOURNAL OF MATHEMATICAL MODELS AND METHODS IN APPLIED SCIENCES
- Journal Indexes: EBSCO Education Source, Index Chemicus (IC), Index Copernicus
- Page Numbers: pp.148-154
- Open Archive Collection: AVESIS Open Access Collection
- Istanbul University Affiliated: Yes
Abstract
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.