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Best Basis Representations with Prior Statistical Models

  • David Leporini
  • Jean-Christophe Pesquet
  • Hamid Krim
Part of the Lecture Notes in Statistics book series (LNS, volume 141)

Abstract

Wavelet packets and local trigonometric bases provide an efficient framework and fast algorithms to obtain a “best representation” of a deterministic signal. Applying these deterministic search techniques to stochastic signals may, however, lead to statistically unreliable results. In this chapter, we revisit this problem and introduce prior models on the underlying signal in noise. We propose several techniques to derive the prior parameters and develop a Bayesian-based approach to the best basis problem. As illustrated by applications to signal denoising, this leads to reduced estimation errors while preserving the classical tree search algorithm.

Keywords

Probability Density Function Reconstruction Error Wavelet Packet Prior Model Decomposition Tree 
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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Copyright information

© Springer Science+Business Media New York 1999

Authors and Affiliations

  • David Leporini
  • Jean-Christophe Pesquet
  • Hamid Krim

There are no affiliations available

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