Exact and Approximate Bayesian Solutions for Inference about Variance Components and Multivariate Inadmissibility.

Abstract

Exact and approximate Bayesian solutions for inference about variance components and multivariate inadmissibility are obtained, using a class of prior distributions which allows realistic forms of prior knowledge to be incorporated. Results are based upon new mathematical representations for the Appell and ordinary hypergeometric functions. (Author)

Document Details

Document Type
Technical Report
Publication Date
Feb 01, 1976
Accession Number
ADA025452

Entities

People

  • Bruce M. Hill

Organizations

  • University of Michigan

Tags

DTIC Thesaurus Topics

  • Complex Variables
  • Functions (Mathematics)
  • Hypergeometric Functions
  • Mathematical Analysis

Fields of Study

  • Mathematics

Readers

  • Operations Research
  • Statistical inference.

Technology Areas

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