Bayesian Estimation of One Dimensional Discrete Markov Random Fields.

Abstract

This document presents two deterministic algorithms for the maximum a posteriori estimation of a one dimensional, binary Markov random field from noisy observations. Extensions to other related problems, such as one dimensional signal matching, and estimation of continuous valued Markov random fields are also discussed. Finally, the author presents an experimental comparison of the performance of optimal algorithms with a stochastic approximation scheme (simulated annealing). Additional keywords: Mathematical models, Dynamic programming, Gaussian noise, White noise, Army research. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1984
Accession Number
ADA151503

Entities

People

  • J. L. Marroquin

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Annealing
  • Artificial Intelligence
  • Computations
  • Computer Programming
  • Computer Vision
  • Dynamic Programming
  • Gaussian Noise
  • Image Processing
  • Image Segmentation
  • Mathematical Models
  • Models
  • Observation
  • Probability
  • Random Variables
  • Statistical Analysis
  • White Noise

Fields of Study

  • Engineering

Readers

  • Computer Vision.
  • Operations Research

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms