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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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21#
發(fā)表于 2025-3-25 04:51:24 | 只看該作者
978-0-387-98885-6Springer Science+Business Media New York 1999
22#
發(fā)表于 2025-3-25 10:26:30 | 只看該作者
23#
發(fā)表于 2025-3-25 14:50:11 | 只看該作者
Spectral View of Wavelets and Nonlinear Regressiond power of the wavelet approach comes from a discrete spectral analysis point of view. Some of the ideas here are the same as some introduced in Chapter 1, but the different viewpoint is intended to give additional insights.
24#
發(fā)表于 2025-3-25 15:52:25 | 只看該作者
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 deblurring of satellite images.
25#
發(fā)表于 2025-3-25 21:17:54 | 只看該作者
26#
發(fā)表于 2025-3-26 00:38:04 | 只看該作者
Bayesian Inference in Wavelet-Based Models978-1-4612-0567-8Series ISSN 0930-0325 Series E-ISSN 2197-7186
27#
發(fā)表于 2025-3-26 06:34:11 | 只看該作者
28#
發(fā)表于 2025-3-26 11:07:16 | 只看該作者
29#
發(fā)表于 2025-3-26 15:50:27 | 只看該作者
Jennifer A. Miller,Brendan Hoover 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 deblurring of satellite images.
30#
發(fā)表于 2025-3-26 20:39:58 | 只看該作者
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