Performance Evaluation of Gibbs Sampling for Bayesian Extracting Sinusoids - (:unkn) unknown (SPRINGER, 2014)
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 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.
Keywords: Bayesian inference; Parameter estimation; Gibbs sampling; Cramer-Rao lower bound and Power spectral density; PARAMETER-ESTIMATION; MONTE-CARLO; DISTRIBUTIONS; SIMULATION; FREQUENCY; INFERENCE; MODEL