An Evaluation of a Bayesian Approach to Compute Estimates-at-Completion for Weapon System Programs.

Abstract

The Bayesian model developed to predict costs-at-completion on weapon system programs is an extension of research done by M. Zaki El-Sabban. The model assumes cost is a random variable and is normally distributed. Budgeted costs are used to develop the prior probability distribution. Actual cost information is used for the Bayesian updating of the probability distribution. The mean of the updated probability distribution is the new estimated cost-at-completion for the program. The model was compared with a non-linear regression model and a linear extrapolation model on five weapon system programs. On three of the programs the non-linear regression model estimated the final cost the greater percentage of the time. On the remaining two programs the Bayesian model estimated the final cost the greater percentage of the time. The Bayesian model demonstrated several advantages over previous models: use at the beginning of the program, inclusion of subjective information, and giving weight to future program budgets.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1977
Accession Number
ADA056502

Entities

People

  • Richard A. Hayes

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Accuracy
  • Air Force
  • Bayesian Networks
  • Computer Programs
  • Control Systems
  • Cost Analysis
  • Data Analysis
  • Economic Forecasting
  • Information Processing
  • Information Science
  • Information Systems
  • Materials
  • Probability
  • Probability Distributions
  • Random Variables
  • Regression Analysis
  • Systems Management

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Computational Modeling and Simulation
  • Life Cycle Cost Analysis

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference