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Spherical Minimum Description Length.

Entropy (Basel, Switzerland)ยท2020
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Updated: Aug 20, 2025

Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level
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Transversality Conditions for Geodesics on the Statistical Manifold of Multivariate Gaussian Distributions.

Trevor Herntier1, Adrian M Peter1

  • 1Department of Computer Engineering and Sciences, Florida Institute of Technology, 150 W. University Blvd., Melbourne, FL 32940, USA.

Entropy (Basel, Switzerland)
|November 24, 2022
PubMed
Summary

This study finds the closest Gaussian distribution on a constraint surface using calculus of variations. It reveals insights into uncertainty evolution along learned geodesic paths.

Keywords:
Fisher informationdifferential geometrygeodesicmultivariate Gaussiantransversality

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Area of Science:

  • Statistics
  • Machine Learning
  • Differential Geometry

Background:

  • The multivariate Gaussian distribution is fundamental in statistics and machine learning.
  • Finding shortest paths (geodesics) on the Gaussian manifold is crucial for understanding distribution relationships.
  • Existing methods often require parameter restrictions for closed-form solutions.

Purpose of the Study:

  • To develop a method for finding the closest multivariate Gaussian distribution on a constraint surface to a given distribution.
  • To explore geodesics on the Gaussian manifold with variable endpoints.
  • To analyze intermediate distributions along geodesics for insights into uncertainty evolution.

Main Methods:

  • Utilizing calculus of variations with a variable endpoint.
  • Applying techniques to search for distributions within a constrained parameter manifold.
  • Examining intermediate distributions along learned geodesic paths.

Main Results:

  • A novel approach to identify the nearest Gaussian distribution under constraints.
  • Demonstration of uncertainty evolution along geodesic paths.
  • Empirical validation using 1D and 2D Gaussian distributions with visual illustrations.

Conclusions:

  • The calculus of variations provides a flexible framework for geodesic pathfinding on Gaussian manifolds.
  • Intermediate distributions offer valuable insights into how uncertainty changes.
  • The method is effective for constrained optimization problems involving Gaussian distributions.