Probabilistic Modeling and Statistical Inference for Random Fields and Space-Time Processes.

Abstract

In this report we summarize the research effort, funded under ONR Grant N00014-91-J-1004, on probabilistic modeling and statistical inference for random fields and space-time processes. The research we have pursued in this project consists of the investigation of a set of interrelated topics involving the development of new mathematical methods for the challenging problems of random field analysis and inference and the application of these methods to problems of practical significance. In particular, the problems of interest in this work were motivated by and directly address issues that are central both to the challenging large-scale data assimilation and estimation problems arising in physical oceanography and to a number of other remote sensing and imaging problems of direct interest to the Navy. (AN)

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Document Details

Document Type
Technical Report
Publication Date
Jan 15, 1995
Accession Number
ADA289956

Entities

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Space

DTIC Thesaurus Topics

  • Algorithms
  • Computer Graphics
  • Detection
  • Detectors
  • Differential Equations
  • Image Processing
  • Information Science
  • Inverse Problems
  • Inverse Scattering
  • Measurement
  • Oceanography
  • Partial Differential Equations
  • Remote Sensing
  • Signal Processing
  • Statistical Inference
  • Synthetic Aperture Radar
  • Two Dimensional

Readers

  • Ocean-Atmosphere Mesoscale Modeling, Data Assimilation, and Flux Boundary Layers
  • Statistical inference.
  • Technical Research and Report Writing.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • Space