Bayesian Statistical Estimation in the Finite State Markov Renewal Process.

Abstract

This paper develops Bayesian statistical estimation procedures for the finite state Markov renewal process. The general case is treated where uncertainty exists about both the waiting time distributions and the transition probabilities. This work extends the Bayesian results of Martin, who only considers the Markov chain case, and Brock, who assumes that the waiting time distributions are known. Moore and Pyke deal only with classical estimation methods. The sampling schemes considered are either to observe n transitions and their associated waiting times, or, more generally, to observe the process for some time T, where T is not necessarily a transition time.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1977
Accession Number
ADA131784

Entities

People

  • George T. Duncan

Organizations

  • Carnegie Mellon University

Tags

DTIC Thesaurus Topics

  • Computational Science
  • Data Science
  • Distribution Functions
  • Geographic Regions
  • Information Science
  • Markov Chains
  • Markov Processes
  • Normal Distribution
  • Probability
  • Random Variables
  • Sampling
  • Social Sciences
  • Statistical Algorithms
  • Statistical Estimation
  • Statistical Inference
  • Statistics
  • Stochastic Processes

Fields of Study

  • Mathematics

Readers

  • Statistical inference.
  • Theoretical Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms