Markov Dependence in Statistics and Information Theory and Statistical Problems in Physical Mapping

Abstract

Investigations were carried out on Markov dependence in statistics and information theory, and statistical problems in physical mapping. Results were obtained on the Minimum Description Length Principle and statistical inference, adaptive quantization in image compression, Markov chain Monte Carlo methods, and statistical problems in the Human Genome Project. These results shed light on the connections between information theory and statistics, on the role of parametric models in quantization and image compression, on understanding the convergence behaviors of Markov chain Monte Carlo samplers, and on the information needed for a clone map of chromosomes. Furthermore, a wavelet image coder is designed as part of the investigation and it gives an excellent performance on test images.

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Document Details

Document Type
Technical Report
Publication Date
May 31, 1998
Accession Number
ADA366138

Entities

People

  • Bin Yu

Organizations

  • University of California, Berkeley

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Algorithms
  • Chromosomes
  • Compression
  • Computer Science
  • Convergence
  • Data Compression
  • Data Science
  • Estimators
  • Human Genome
  • Image Compression
  • Information Science
  • Information Theory
  • Markov Chains
  • Monte Carlo Method
  • Statistical Algorithms
  • Statistical Inference
  • Statistics

Readers

  • Image Processing and Computer Vision.
  • Mathematical Modeling and Probability Theory.
  • Statistical inference.

Technology Areas

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