Multivariate Probability Assessment.

Abstract

In order that formal Bayesian decision analysis may be applied to a specific decision problem, probabilities must be assigned to the associated uncertain quantities. Very often, the only available, relevant information concerning these uncertain quantities' probabilities is an expert's opinion, which may be represented mathematically by what is known as a judgmental probability. The assessment of judgmental probabilities involves numerous psychological and analytical problems. The first part of this report provides a concise, well referenced summary of previous work on the assessment of judgmental probabilities involving one uncertain quantity. The rest of the paper directly concerns the development of techniques for multivariate, judgmental probability assessment, i.e., assessing a joint probability distribution of two or more uncertain quantities. A major portion of this research investigates the use of the mutual probabilistic independence property in multivariate probability assessment. Also, a technique for the assessment of two dependent, uncertain quantities is developed. The method derives a representation of the desired joint probability density function, in the form of a set of two dimensional slices of its surface, from assessed marginal and cumulative distributions. (Author)

Document Details

Document Type
Technical Report
Publication Date
May 01, 1971
Accession Number
AD0731761

Entities

People

  • William Owen Robinson

Organizations

  • Massachusetts Institute of Technology

Tags

DTIC Thesaurus Topics

  • Data Science
  • Information Science
  • Mathematics
  • Probability
  • Probability Density Functions
  • Probability Distributions
  • Random Variables
  • Two Dimensional

Fields of Study

  • Mathematics

Readers

  • Regression Analysis.
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference