Information Geometry: Geometrization of Science of Information

Abstract

Information geometry is an emerging mathematical framework for modeling the space ofprobability density functions as forming a (possibly infinite-dimensional) differentiablemanifold equipped with a Riemannian metric and a pair of conjugate connections thatneed not be metrical. Information geometry has so far been applied to many disciplinessuch as asymptotic statistics, Bayesian inference, information theory and coding, machinelearning, neural computation, econometrics, cognitive psychology, etc. It is seen by someas a potentially unifying framework for geometrizing information in the same way thatphysics has been geometrized.

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Document Details

Document Type
Technical Report
Publication Date
Oct 14, 2017
Accession Number
AD1097196

Entities

People

  • Jun Zhang

Organizations

  • University of Michigan

Tags

Communities of Interest

  • Autonomy
  • Human Systems

DTIC Thesaurus Topics

  • Algorithms
  • Banach Space
  • Bayesian Inference
  • Complex Variables
  • Data Science
  • Differential Geometry
  • Functional Analysis
  • Geometry
  • Information Processing
  • Information Science
  • Information Systems
  • Information Theory
  • Machine Learning
  • Psychology
  • Statistical Inference
  • Statistics
  • Students

Readers

  • Graph Algorithms and Convex Optimization.
  • Neural Network Machine Learning.

Technology Areas

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