A Bayesian View of Assessing Uncertainty and Comparing Expert Opinion

Abstract

A Bayesian approach to the problem of comparing experts or expert systems is presented. The question of who is an expert is considered and comparisons among well-calibrated experts are studied. The concept of refinement, in various equivalent forms, is used in this study. An informative example of the combination of the opinions of well-calibrated experts is described. Total orderings of the class of well-calibrated experts are derived from strictly proper scoring rules. Keywords: predictions; Forecasters; Well calibrated; Expert systems; Combining opinion; Scoring rules.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Mar 16, 1987
Accession Number
ADP005295

Entities

People

  • Morris H. Degroot

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Bayesian Networks
  • Computer Programs
  • Decision Theory
  • Delphi Method
  • Discrete Distribution
  • Expert Systems
  • Fuzzy Logic
  • Fuzzy Sets
  • Intervals
  • New York
  • Probability
  • Probability Distributions
  • Random Variables
  • Statistical Decision Theory
  • Statistics
  • Theorems

Readers

  • Computational Modeling and Simulation
  • Educational Psychology
  • Military Leadership and Professional Education.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference