Modeling and Estimating Uncertainty in Parameter Estimation

Abstract

In this paper we discuss questions related to reliability or variability of estimated parameters in deterministic least squares problems. By viewing the parameters for the inverse problem as realizations for a random variable we are able to use standard results from probability theory to formulate a tractable probabilistic framework to treat this uncertainty. We discuss method stability and approximate problems and are able to show convergence of solutions of the approximate problems to those of the original problem. The efficacy of our approach is demonstrated in numerical examples involving estimation of constant parameters in differential equations.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1999
Accession Number
ADA447550

Entities

People

  • H. Thomas Banks
  • Kathleen L. Bihari

Organizations

  • North Carolina State University

Tags

DTIC Thesaurus Topics

  • Air Force
  • Algorithms
  • Computational Science
  • Convergence
  • Delta Functions
  • Discrete Distribution
  • Distribution Functions
  • Equations
  • Gaussian Distributions
  • Inverse Problems
  • Numerical Analysis
  • Probability
  • Probability Distributions
  • Random Variables
  • Statistical Analysis
  • Topology
  • Uncertainty

Fields of Study

  • Mathematics

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.