Bayes Decision Rules Based on Objective Priors. Part I. Formulation and Application. Part 2. Justification

Abstract

The problem of statistical decision making under uncertainty is considered. A Bayes approach based upon prior probabilities which are found using an objective inference technique developed by R. L. Kashyap is proposed as the basic solution procedure. The problem is formulated in a statistical decision theory format and the general solution technique outlined. Several examples are solved to illustrate its application. Using this inference technique, it is possible to have different priors for different experiments. A general decision criterion is formulated to handle these situations.

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 1972
Accession Number
AD0747506

Entities

People

  • R. L. Kashyap
  • T. L. Oberlin

Organizations

  • Purdue University

Tags

DTIC Thesaurus Topics

  • Air Force
  • Decision Theory
  • Electrical Engineering
  • Engineering
  • Frequency
  • Observation
  • Probability
  • Probability Distributions
  • Random Variables
  • Sensitivity
  • Standards
  • Statistical Decision Theory
  • Uncertainty
  • Zero-Sum Games

Fields of Study

  • Mathematics

Readers

  • Operations Research
  • Statistical inference.

Technology Areas

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