On Optimal Descriminants Between Two Classes of Random Variables in Terms of the Moments of Their Distributions.

Abstract

For many problems of interest in statistical pattern recognition, density estimates for a random variable X of dimension d are unreliable unless the number of sample vectors is very large. For even moderately large sample sizes are often insufficient, however, lower order moments may be accurately estimated. In this paper we are concerned with the problem of optimally discriminating between two classes of random variables in terms of the available information about them of reasonable accuracy (their lower order moments). In no case do we make any assumption about the form of the probability densities of random variables X. (We do in some cases assume certain forms for the densities of functions of these random variables L(X).) (Author)

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Document Details

Document Type
Technical Report
Publication Date
Feb 28, 1979
Accession Number
ADA068551

Entities

People

  • Lee K. Jones

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Coefficients
  • Errors
  • Hypotheses
  • Probability
  • Random Variables
  • Sequences
  • Two Dimensional

Fields of Study

  • Mathematics

Readers

  • Statistical inference.

Technology Areas

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