A Bayesian Method to Improve Sampling in Weapons Testing

Abstract

This thesis describes a Bayesian method to determine the number of samples needed to estimate a proportion or probability with 95% confidence when prior bounds are placed on that proportion. It uses the Uniform (a,b) distribution as the prior, and develops a computer program and tables to find the sample size. Tables and examples are also given to compare these results with other approaches to finding sample size. The improvement that can be obtained with this method's fewer samples, and consequently less cost in Weapons Testing is required to meet a desired confidence size for a proportion or probability.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1988
Accession Number
ADA204365

Entities

People

  • Theodore C. Floropoulos

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Weapons Technologies

DTIC Thesaurus Topics

  • Accuracy
  • Bayesian Networks
  • Computer Programs
  • Computers
  • Confidence Limits
  • Detection
  • Distribution Functions
  • Operations Research
  • Probability
  • Probability Distributions
  • Random Variables
  • Reliability
  • Sampling
  • Schools
  • Statistical Inference
  • Statistics
  • Weapon Systems

Fields of Study

  • Computer science
  • Mathematics

Readers

  • Aerospace logistics and air mobility.
  • Statistical inference.

Technology Areas

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