Modeling Expert Opinion: Likelihoods under Incomplete Probabilistic Specification

Abstract

Expert opinion is often sought with regard to unknowns in a decision-making setting. The presumption is that such opinion is elicited as an incomplete probabilistic specification either in the form of probability assignments to fixed intervals or in the form of selected quantiles. The authors present likelihoods for such a specification that arise through random mixtures of Beta distributions. They presume that a supra Bayesian presides over the opinion collection resulting in the posterior distribution as the mechanism for pooling opinion. The models are applied to opinions collected regarding points per game for participants in the 1991 NBA championship basketball series.

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Document Details

Document Type
Technical Report
Publication Date
Dec 09, 1992
Accession Number
ADA258775

Entities

People

  • Alan E. Gelfand
  • Bani K. Mallick
  • Dipak K. Dey

Organizations

  • Stanford University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Bayesian Networks
  • Covariance
  • Data Science
  • Delphi Method
  • Discrete Distribution
  • Information Science
  • Intervals
  • Monte Carlo Method
  • New York
  • Probabilistic Models
  • Probability
  • Probability Distributions
  • Random Variables
  • Specifications
  • Statistical Analysis
  • Statistics
  • United States

Fields of Study

  • Mathematics

Readers

  • Educational Psychology
  • Game Theory.
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference