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

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0930-0325 ce of numerous referees to whom we are most indebted. We are also grateful to John Kimmel and the Springer-Verlag referees for considering our proposal in a ver978-0-387-98885-6978-1-4612-0567-8Series ISSN 0930-0325 Series E-ISSN 2197-7186

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MCMC Methods in Wavelet Shrinkage: Non-Equally Spaced Regression, Density and Spectral Density Estimr vanishing coefficients. This implements wavelet coefficient thresholding as a formal Bayes rule. For non-zero coefficients we introduce shrinkage by assuming normal priors. Allowing different prior variance at each level of detail we obtain level-dependent shrinkage for non-zero coefficients..We i

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Book 1999eree and critically evaluate the papers which were submitted for inclusion in this volume. For this substantial task, we relied on the service of numerous referees to whom we are most indebted. We are also grateful to John Kimmel and the Springer-Verlag referees for considering our proposal in a ver

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An Introduction to Waveletse and below the .-axis. The diminutive connotation of . suggest the function has to be well localized. Other requirements are technical and needed mostly to ensure quick and easy calculation of the direct and inverse wavelet transform.

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