Computation of Invariant Measures and Stationary Expectations for Markov Chains with Block‐Band Transition Matrix

Hendrik Baumann and Thomas Hanschke

Journal of Applied Mathematics, 2020, vol. 2020, issue 1

Abstract: This paper deals with the computation of invariant measures and stationary expectations for discrete‐time Markov chains governed by a block‐structured one‐step transition probability matrix. The method generalizes in some respect Neuts’ matrix‐geometric approach to vector‐state Markov chains. The method reveals a strong relationship between Markov chains and matrix continued fractions which can provide valuable information for mastering the growing complexity of real‐world applications of large‐scale grid systems and multidimensional level‐dependent Markov models. The results obtained are extended to continuous‐time Markov chains.

Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1155/2020/4318906

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:jnljam:v:2020:y:2020:i:1:n:4318906

Access Statistics for this article

More articles in Journal of Applied Mathematics from John Wiley & Sons
Bibliographic data for series maintained by Wiley Content Delivery ().