Detecting and Estimating the Location of Shifts in the Transition Probabilities of Two-State Markov Chains.

Abstract

K independent and identically distributed two-state Markov chains are subjected to shifts in their transition probabilities, which occur at unknown time points. Two detection and estimation procedures, concerning the location of the shift, are developed in a Bayesian framework for an at-most-one-change model. Estimates of the operating characteristics of these procedures have been obtained by simulating such Markov chains. The simulation results indicate that the proposed procedure, which is based on comparison of posterior risks, could be very effective. (Author)

Document Details

Document Type
Technical Report
Publication Date
May 15, 1971
Accession Number
AD0723523

Entities

People

  • Shelemyahu Zacks

Organizations

  • Case Western Reserve University

Tags

DTIC Thesaurus Topics

  • Bayesian Networks
  • Computational Science
  • Detection
  • Markov Chains
  • Mathematics
  • Models
  • Probabilistic Models
  • Probability
  • Simulations
  • Transitions

Fields of Study

  • Mathematics

Readers

  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - DoD AI Strategy