Accession Number:

ADA455852

Title:

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

Descriptive Note:

Research paper

Corporate Author:

MASSACHUSETTS INST OF TECH CAMBRIDGE LAB FOR INFORMATION AND DECISION SYSTEMS

Personal Author(s):

Report Date:

1988-08-01

Pagination or Media Count:

23.0

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

Subject Categories:

  • Numerical Mathematics
  • Statistics and Probability

Distribution Statement:

APPROVED FOR PUBLIC RELEASE