Estimation and Prediction of Nonlinear Functionals of Gaussian Processes.

Abstract

The general estimation problem of nonlinear functionals of Gaussian processes is solved via the tensor product structure of nonlinear space in the sense that the nonlinear problem reduces to a standard linear estimation problem, the theory of which has been well developed. Also introduced the concept of super predictor for a class of prediction problems and a lower bound for the mean square error of the nonlinear prediction is derived.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1977
Accession Number
ADA040309

Entities

People

  • Stamatis Cambanis
  • Steel T. Huang

Organizations

  • University of North Carolina at Chapel Hill

Tags

DTIC Thesaurus Topics

  • Air Force
  • Computing-Related Activities
  • Data Science
  • Gaussian Processes
  • Hilbert Space
  • Information Science
  • Markov Processes
  • Mathematical Analysis
  • Mathematics
  • Polynomials
  • Probability
  • Random Variables
  • Standards
  • Stationary
  • Statistical Analysis
  • Statistics
  • Stochastic Processes

Readers

  • Computational Modeling and Simulation
  • Finite Element Method (FEM) for solving Partial Differential Equations (PDEs)
  • Mathematical Modeling and Probability Theory.

Technology Areas

  • Space