Performance Analysis of Gibbs Sampling for Bayesian Extracting Sinusoids-Reference-Cited by-同舟云学术

Performance Analysis of Gibbs Sampling for Bayesian Extracting Sinusoids

Published:2021-11-23 Issue: Volume:15 Page:148-154
ISSN:1998-0140
Container-title:International Journal of Mathematical Models and Methods in Applied Sciences
language:en
Short-container-title:

Author:

Cevri Mehmet¹,Üstündag Dursun²

Affiliation:

1. Department of Mathematics, Istanbul University, Istanbul, Turkey

2. Department of Mathematics, Marmara University, Istanbul, Turkey

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.

Publisher

North Atlantic University Union (NAUN)

Subject

Applied Mathematics,Computational Mathematics,Mathematical Physics,Modeling and Simulation

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