Bayesian Interpolation and Deconvolution

Abstract

The deconvolution problem is addressed in stages beginning with the interpolation problem when little prior information is available and proceeding to the full deconvolution problem when a great deal of prior information is available. The results of these calculations indicate that good solutions to the deconvolution program are available even when limited prior information is available and that these results overlap those obtained when a great deal of prior information is available. The difference between them is that the use of uninformative priors causes large uncertainties in the estimated signal, while highly informative priors decreases the uncertainties in the signal.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1992
Accession Number
ADA256185

Entities

People

  • G. L. Bretthorst

Organizations

  • University of Washington

Tags

Communities of Interest

  • C4I
  • Cyber
  • Materials and Manufacturing Processes
  • Sensors
  • Weapons Technologies

DTIC Thesaurus Topics

  • Amplitude
  • Bayes Theorem
  • Boundaries
  • Delta Functions
  • Detectors
  • Eigenvalues
  • Eigenvectors
  • Engineering
  • Equations
  • Information Theory
  • Integrals
  • Peak Values
  • Probability
  • Probability Density Functions
  • Probability Distributions
  • Statistics
  • Universities

Readers

  • Regression Analysis.
  • Theoretical Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference