Bayesian Wavelet Shrinkage Strategies: A Review

  • Norbert ReményiEmail author
  • Brani Vidakovic


In this chapter the authors overview recent developments and current status of use of Bayesian paradigm in wavelet shrinkage. The paradigmatic problem where wavelet shrinkage is employed is that of nonparametric regression where data are modeled as observations from an unknown signal contaminated with a Gaussian noise. Bayes rules as general shrinkers provide a formal mechanism to implement shrinkage in the wavelet domain that is model based and adaptive. New developments including dependence models, complex wavelets and MCMC strategies are described. Applications include inductance plethysmography data and curve classification procedure applied in botany. The chapter features an extensive set of references consisting of almost 100 entries.


Markov Chain Monte Carlo Discrete Wavelet Transformation Wavelet Coefficient Complex Wavelet Posterior Median 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer New York 2013

Authors and Affiliations

  1. 1.School of Industrial and Systems EngineeringGeorgia Institute of TechnologyAtlantaUSA
  2. 2.School of Biomedical EngineeringGeorgia Institute of TechnologyAtlantaUSA

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