Title :
Bayesian interpretation of periodograms
Author :
Giovannelli, Jean-François ; Idier, Jérôme
Author_Institution :
Lab. des Signaux et Syst., SUPELEC, Gif-sur-Yvette, France
fDate :
7/1/2001 12:00:00 AM
Abstract :
The usual nonparametric approach to spectral analysis is revisited within the regularization framework. Both usual and windowed periodograms are obtained as the squared modulus of the minimizer of regularized least squares criteria. Then, particular attention is paid to their interpretation within the Bayesian statistical framework. Finally, the question of unsupervised hyperparameter and window selection is addressed. It is shown that maximum likelihood solution is both formally achievable and practically useful
Keywords :
Bayes methods; least squares approximations; maximum likelihood estimation; spectral analysis; Bayesian interpretation; Bayesian statistics; maximum likelihood solution; nonparametric approach; parameter estimation; regularization; regularized least squares criteria; spectral analysis; squared modulus; unsupervised hyperparameter selection; unsupervised window selection; windowed periodograms; Amplitude estimation; Bayesian methods; Books; Fourier transforms; Frequency; Least squares methods; Maximum likelihood estimation; Shape; Signal analysis; Spectral analysis;
Journal_Title :
Signal Processing, IEEE Transactions on
