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Multiscale Bayesian Estimation and Data Rectification

  • Sridhar Ungarala
  • Bhavik R. Bakshi
Part of the Computational Imaging and Vision book series (CIVI, volume 19)

Abstract

Most measured data contain features localized at multiple scales in time and frequency. Multiscale approaches to state estimation and data rectification offer attractive alternatives to traditional single scale methods by exploiting the ability of wavelets to approximately decorrelate many autocorrelated stochastic processes and extract deterministic features in a signal. This chapter describes several important features of the framework, (a) a Bayesian approach for efficiently fusing prior information with measured data subject to process model constraints, (b) the ability of wavelets to extract temporal dynamics in scale leading to efficient rectification methods for processes lacking accurate models, (c) multiscale linear models to describe the time—scale localized evolution of the system leading to adaptive estimation in time and scale and (d) parallelizable and computationally efficient algorithms. Most existing single scale methods as well their multiscale analogues are shown to be special cases of the general multiscale Bayesian approach.

Keywords

Fine Scale Wavelet Coefficient Data Rectification Coarse Scale Single Scale 
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 Dordrecht 2001

Authors and Affiliations

  • Sridhar Ungarala
    • 1
  • Bhavik R. Bakshi
    • 2
  1. 1.Department of Chemical EngineeringCleveland State UniversityClevelandUSA
  2. 2.Department of Chemical EngineeringThe Ohio State UniversityColumbusUSA

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