Estimation in the Presence of Noise of a Signal Which Is Flat except for Jumps. Part I. A Bayesian Study.

Abstract

Consider the problem of estimating, in a Bayesian framework and in the presence of additive Gaussian noise, a signal which is a random step function. The best linear estimates, the Bayes estimates and the estimates with known change points are derived, evaluated and compared analytically and numerically. A characterization of the Bayes estimates is presented. This characterization has a reasonable interpretation and also provides a way to compute the Bayes estimates with a number of operations of the order of T where T is the fixed time span. An approximation to the Bayes estimates is proposed which is reasonably good and reduces the total number of operations to the order of T. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 1982
Accession Number
ADA124115

Entities

People

  • Yi-ching Yao

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • C4I
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Additives (Chemicals)
  • Bayesian Networks
  • Cis
  • Computational Science
  • Filtration
  • Gaussian Noise
  • Models
  • Noise
  • Normal Distribution
  • Probabilistic Models
  • Probability
  • Probability Distributions
  • Random Variables
  • Simulations
  • Step Functions
  • Stochastic Processes
  • White Noise

Fields of Study

  • Mathematics

Readers

  • Aerodynamics.
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference