Frequentist and Bayesian inference for Gaussian–log‐Gaussian wavelet trees and statistical signal processing applications

We introduce new estimation methods for a subclass of the Gaussian scale mixture models for wavelet trees by Wainwright, Simoncelli and Willsky that rely on modern results for composite likelihoods and approximate Bayesian inference. Our methodology is illustrated for denoising and edge detection pr...

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Veröffentlicht in:	Stat (International Statistical Institute) 2017, Vol.6 (1), p.248-256
Hauptverfasser:	Jacobsen, Robert Dahl, Møller, Jesper
Format:	Artikel
Sprache:	eng
Schlagworte:	Bayesian analysis conditional auto‐regression Edge detection EM algorithm Gaussian process hidden Markov tree Image detection integrated nested Laplace approximations Noise reduction Signal processing Statistical inference Trees Wavelet analysis
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Beschreibung
Zusammenfassung:	We introduce new estimation methods for a subclass of the Gaussian scale mixture models for wavelet trees by Wainwright, Simoncelli and Willsky that rely on modern results for composite likelihoods and approximate Bayesian inference. Our methodology is illustrated for denoising and edge detection problems in two‐dimensional images. Copyright © 2017 John Wiley & Sons, Ltd.
ISSN:	2049-1573 2049-1573
DOI:	10.1002/sta4.156