PROGRAMS FOR COMPUTING THE STUDENT DISTRIBUTION AND RELATED BAYESIAN FUNCTIONS

Abstract

This report contains programs used in computing the tables presented in Jerome Bracken and Arthur Schleifer, Jr., Tables for Normal Sampling with Unknown Variance: The Student Distribution and Economically Optimal Sampling Plans, Division of Research, Graduate School of Business Administration, Harvard University, 1964. The tables are essentially of two kinds: tables of the ordinary student 't' density and cumulative functions, and tables to facilitate Bayesian analysis of certain commonly occurring decision problems in which sampling may or may not be involved. The programs given in this report could be used to compute tables for parameter values other than those of the book either by reading in alternative data, or by straightforward modification where the parameters of the book are included in the programs. The programs are written in FORTRAN II, and in computing the tables in the book they were used on the IBM 7090 and IBM 1401. It should be noted that new programs have been written to perform some of the computations faster, more accurately, or more efficiently.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
May 10, 1965
Accession Number
AD0463576

Entities

People

  • Arthur Schleifer Jr.
  • Jerome Bracken

Organizations

  • George Washington University

Tags

Communities of Interest

  • Biomedical
  • C4I
  • Energy and Power Technologies
  • Human Systems

DTIC Thesaurus Topics

  • Business Administration
  • Classification
  • Commerce
  • Computational Science
  • Computer Programs
  • Computers
  • Contracts
  • Government Procurement
  • Governments
  • Infinite Series
  • Military Research
  • Numerical Analysis
  • Sampling
  • Security
  • Students
  • United States
  • Universities

Fields of Study

  • Education
  • Mathematics

Readers

  • Computer Science.
  • Military History of the United States in the 20th Century.
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference