Estimation in the Presence of Noise of a Signal Which is Flat Except for Jumps. Part II. The Empirical Bayes Approach.

Abstract

This is the second of a two-part paper. In the first part Yao (1982), a special Bayesian Model A is studied in detail. In this part, a more general model is proposed and studied in an empirical Bayes framework. The results for Model A are applied to step-function signals using the ideas of empirical Bayes and maximum likelihood applied to the parameters of the Bayesian Model A. An efficient computational method is proposed to approximate the likelihood function under Model A. Several empirical Bayes estimators of the unknown step-function signal are compared by simulation. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1983
Accession Number
ADA124116

Entities

People

  • Yi-ching Yao

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Bayesian Networks
  • Computational Science
  • Computations
  • Computer Simulations
  • Estimators
  • Maximum Likelihood Estimation
  • Method Of Moments
  • Models
  • Noise
  • Probability
  • Random Variables
  • Sequences
  • Simulations
  • Standards
  • Step Functions
  • United States
  • United States Government

Fields of Study

  • Mathematics

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Business Analytics
  • Computational Modeling and Simulation

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms