Titlebook: Bayesian Inference in Wavelet-Based Models; Peter Müller,Brani Vidakovic Book 1999 Springer Science+Business Media New York 1999 Markov mo

Restenosis

Bayesian Approach to Wavelet Decomposition and Shrinkageel for its wavelet coefficients by establishing a relationship between the hyperparameters of the proposed model and the parameters of those Besov spaces within which realizations from the prior will fall. Such a relation may be seen as giving insight into the meaning of the Besov space parameters t

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FLING

Isthmus

Facilities

Geyser

Minimax Restoration and Deconvolution we study linear and non-linear diagonal estimators in an orthogonal basis. General conditions are given to build nearly minimax optimal estimators with a thresholding in an orthogonal basis. The deconvolution of bounded variation signals is studied in further details, with an application to the deb

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玷污

Best Basis Representations with Prior Statistical Modelssignal. Applying these deterministic search techniques to stochastic signals may, however, lead to statistically unreliable results. In this chapter, we revisit this problem and introduce prior models on the underlying signal in noise. We propose several techniques to derive the prior parameters and

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Modeling Dependence in the Wavelet Domainrecursive way to compute the within- and across-level covari-ances. We then show the usefulness of those findings in some of the best known applications of wavelets in statistics. Wavelet shrinkage attempts to estimate a function from noisy data. When approaching the problem from a Bayesian point of

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MCMC Methods in Wavelet Shrinkage: Non-Equally Spaced Regression, Density and Spectral Density Estimity estimation. The common theme in all three applications is the lack of posterior independence for the wavelet coefficients ... In contrast, most commonly considered applications of wavelet decompositions in Statistics are based on a setup which implies . independent coefficients, essentially redu