Computable Error Bounds for Aggregated Markov Chains.

Abstract

This paper describes a method for computing the steady state probability vector of a nearly completely decomposable Markov chain. The method is closely related to one proposed by Simon and Ando and developed by Courtois. However, the method described here does not require the determination of a completely decomposable stochastic approximation to the transition matrix and hence it is applicable to matrices other than stochastic. An error analysis of the procedure is given which results in effectively computable error bounds. (Author)

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Document Details

Document Type
Technical Report
Publication Date
May 01, 1980
Accession Number
ADA086193

Entities

People

  • Gilbert W. Stewart

Organizations

  • University of Maryland

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Computations
  • Computer Science
  • Computers
  • Economic Systems
  • Eigenvalues
  • Eigenvectors
  • Error Analysis
  • Errors
  • Linear Systems
  • Markov Chains
  • Notation
  • Perturbation Theory
  • Perturbations
  • Probability
  • Steady State
  • Theorems

Fields of Study

  • Mathematics

Readers

  • Mathematical Modeling and Probability Theory.
  • Regression Analysis.