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    New Bayesian Model Averaging Framework for Wavelet-Based Signal Processing

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    Author
    Wan, Yi; Nowak, Robert David
    Date
    2001-09-06
    Abstract
    This paper develops a new signal modeling framework using Bayesian model averaging formulation and the redundant or translation-invariant wavelet transform. The aim of this framework is to provide a paradigm general enough to effectively treat fundamental problems arising in wavelet-based signal processing, segmentation, and modeling. Unlike many other attempts to mitigate the translation-dependent nature of wavelet analysis and processing, this framework is based on a well-defined statistical model averaging paradigm and improves over standard translation-invariant schemes for wavelet denoising. In addition to deriving new and more powerful signal modeling and denoising schemes, we demonstrate that certain existing methods are special suboptimal solutions of our proposed model averaging criterion. Experimental results demonstrate the promise of this framework.
    Description
    Conference Paper
    Citation
    Y. Wan and R. D. Nowak, "New Bayesian Model Averaging Framework for Wavelet-Based Signal Processing," 2000.
    Published Version
    http://dx.doi.org/10.1109/ICASSP.2000.862018
    Keyword
    Bayesian; signal modeling framework; wavelet-based signal processing; segmentation; Bayesian; More... signal modeling framework; wavelet-based signal processing; segmentation Less...
    Type
    Conference paper
    Citable link to this page
    https://hdl.handle.net/1911/20438
    Metadata
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    • DSP Publications [508]
    • ECE Publications [1468]

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    Home | FAQ | Contact Us | Privacy Notice | Accessibility Statement
    Managed by the Digital Scholarship Services at Fondren Library, Rice University
    Physical Address: 6100 Main Street, Houston, Texas 77005
    Mailing Address: MS-44, P.O.BOX 1892, Houston, Texas 77251-1892
    Site Map