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Noise Reduction at the Fan Outlet Read chapter Read first chapter

2014 | OriginalPaper | Chapter

2. Performance Evaluation of Gibbs Sampling for Bayesian Extracting Sinusoids

Authors : M. Cevri, D. Üstündag

Published in: Computational Problems in Engineering

Publisher: Springer International Publishing

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Abstract

This 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 Cramér–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.

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Metadata
Title
Performance Evaluation of Gibbs Sampling for Bayesian Extracting Sinusoids
Authors
M. Cevri
D. Üstündag
Copyright Year
2014
Book
Computational Problems in Engineering

Print ISBN: 978-3-319-03966-4

Electronic ISBN: 978-3-319-03967-1

Copyright Year: 2014

DOI
https://doi.org/10.1007/978-3-319-03967-1_2

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