Componentwise bounds for nearly completely decomposable Markov chains using stochastic comparison and reordering

Date
2005
Authors
Pekergin, N.
Dayar T.
Alparslan, D. N.
Advisor
Supervisor
Co-Advisor
Co-Supervisor
Instructor
Source Title
European Journal of Operational Research
Print ISSN
0377-2217
1872-6860
Electronic ISSN
Publisher
Elsevier
Volume
165
Issue
3
Pages
810 - 825
Language
English
Type
Article
Journal Title
Journal ISSN
Volume Title
Series
Abstract

This paper presents an improved version of a componentwise bounding algorithm for the state probability vector of nearly completely decomposable Markov chains, and on an application it provides the first numerical results with the type of algorithm discussed. The given two-level algorithm uses aggregation and stochastic comparison with the strong stochastic (st) order. In order to improve accuracy, it employs reordering of states and a better componentwise probability bounding algorithm given st upper- and lower-bounding probability vectors. Results in sparse storage show that there are cases in which the given algorithm proves to be useful. © 2004 Elsevier B.V. All rights reserved.

Course
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Book Title
Keywords
Aggregation, Markov processes, Near complete decomposability, Reorderings, Stochastic comparison, Algorithms, Eigenvalues and eigenfunctions, Linear equations, Markov processes, Matrix algebra, Poisson distribution, Probability, Problem solving, Queueing networks, Vectors, Markov modulated Poisson process (MMPP), Near complete decomposability, Reordering, Stochastic comparison, Operations research
Citation
Published Version (Please cite this version)