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Get Wavelet Bayesian Block Shrinkage Via Mixtures Of Normal-inverse ... - Isye Gatech

Of Technology Atlanta, GA 30332-0205, USA Abstract In this paper we propose a block shrinkage method in the wavelet domain for estimating an unknown function in the presence of Gaussian noise. This shrinkage utilizes an empirical Bayes, block-adaptive approach that accounts for the sparseness of the representation of the unknown function. The modeling is accomplished distributions as a joint by using a mixture of two normal-inverse gamma prior on wavelet coefficients and noise varianc.

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