Truncated Gaussians as Tolerance Sets.

Abstract

This work focuses on the use of truncated Gaussian distributions as models for bounded data - measurements that are constrained to appear between fixed limits. We prove that the truncated Gaussian can be viewed as a maximum entropy distribution for truncated bounded data, when mean and covariance are given. We present the characteristic function for the truncated Gaussian; from this, we derive algorithms for calculation of mean, variance, summation, application of Bayes rule and filtering with truncated Gaussians. As an example of the power of our methods, we describe a derivation of the disparity constraint (used in computer vision) from our models. Our approach complements results in Statistics, but our proposal is not only to use the truncated Gaussian as a model for selected data; we propose to model measurements as fundamentally in terms of truncated Gaussians.

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Document Details

Document Type
Technical Report
Publication Date
Sep 28, 1994
Accession Number
ADA289357

Entities

People

  • Eric Krotkov
  • Fabio Cozman

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Computer Vision
  • Computers
  • Covariance
  • Data Science
  • Eigenvalues
  • Equations
  • Gaussian Distributions
  • Object Recognition
  • Probability
  • Random Variables
  • Robotics
  • Statistical Analysis
  • Statistics
  • Technical Information Centers
  • Theorems

Fields of Study

  • Computer science

Readers

  • Statistical inference.

Technology Areas

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