Maximum A-Posteriori Estimation of Random Fields - Elliptic Gaussian Fields Observed via a Noisy Channel

Abstract

An extension of the "prior density for path" (Onsager-Machlup functional) is defined and shown to exist for Gaussian fields generated by solutions of elliptic Partial Differential Equations (PDEs) driven by white noise. This functional is then used to define and solve the MAP estimation of such fields observed via nonlinear noisy sensors. Existence results and a representation of the estimator are derived for this model

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1988
Accession Number
ADA455852

Entities

People

  • Amir Dembo
  • Ofer Zeitouni

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Abstracts
  • Boundaries
  • Calculus Of Variations
  • Contracts
  • Difference Equations
  • Eigenvalues
  • Eigenvectors
  • Estimators
  • Gaussian Processes
  • Integrals
  • Military Research
  • Models
  • Noise
  • Observation
  • Random Variables
  • Statistical Algorithms
  • White Noise

Fields of Study

  • Computer science

Readers

  • Calculus or Mathematical Analysis
  • Finite Element Method (FEM) for solving Partial Differential Equations (PDEs)
  • Statistical inference.