A Simulation Analysis of The Effectiveness of Markovian Control and Bayesian Control.

Abstract

Statistical cost control decisions may be based on two competing decision models. The Markovian Control model controls a process by investigating the process whenever the reported cost exceeds a fixed critical limit. The Bayesian Control model controls a process by using the reported cost to update the probability of the process being in-control and investigate the process whenever such posterior probability is less than a fixed critical value. This paper compares the relative effectiveness of the two models by a simulation analysis. It is observed that the Markovian Control model performs as well as or better than the Bayesian Control model unless the cost distribution of the in-control state is more dispersed than that of the out-of-control state. It is also observed that the relative effectiveness of the Markovian Control model compared to the Bayesian Control increases as the savings from an investigation increases when the cost distribution of the in-control state is less dispersed than that of the out-of-control state. (Author)

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1981
Accession Number
ADA104635

Entities

People

  • Soo Sup Song

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Boundaries
  • Computer Programs
  • Computers
  • Dispersions
  • Distribution Functions
  • Intervals
  • Markov Processes
  • Normal Distribution
  • Operations Research
  • Probability
  • Probability Distributions
  • Quality Control
  • Random Number Generators
  • Random Variables
  • Simulations
  • Standards
  • Steady State

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Public Financial Management and Budgeting
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Bayesian Inference