Computational Discrete Time Markov Chain with Correlated Transition Probabilities
DOI : 10.3844/jmssp.2006.457.459
Journal of Mathematics and Statistics
Volume 2, Issue 4
This study presents a computational procedure for analyzing statistics of steady state probabilities in a discrete time Markov chain with correlations among their transition probabilities. The proposed model simply uses the first order Taylor's series expansion and statistical expected value properties to obtain the resulting linear matrix equations system. Computationally, the bottleneck is O(n4) but can be improved by distributed and parallel processing. A preliminary computational experience is reported.
© 2006 Peerayuth Charnsethikul. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.