Lumpability and time reversibility in the aggregation-disaggregation method for large markov chains
- 1 January 1989
- journal article
- research article
- Published by Taylor & Francis in Communications in Statistics. Stochastic Models
- Vol. 5 (1) , 63-81
- https://doi.org/10.1080/15326348908807099
Abstract
The aggregation-disaggregation algorithm of Takahashi (1975) is a rank-reduction method for efficiently computing ergodic probabilities of large Markov chains. It has been shown by Schweitzer (1984) that if a Markov chain is “exactly lumpable”, then the aggregation-disaggregation algorithm converges in one step. In this paper, we show that ordinary lumpability eliminates the aggregation procedure. Furthermore, a new algorithm is developed which produces the ergodic probability vector in one step for a class of Markov chains including the time reversible ones. The idea behind the new algorithm enables one to develop different algorithms for different classes of Markov chains. A preliminary study along this line of research is also discussed.Keywords
This publication has 8 references indexed in Scilit:
- Decomposition and aggregation by class in closed queueing networksIEEE Transactions on Software Engineering, 1986
- An iterative aggregation-disaggregation algorithm for solving linear equationsApplied Mathematics and Computation, 1986
- Iterative aggregation for solving undiscounted semi-markovian reward processesCommunications in Statistics. Stochastic Models, 1986
- Equivalence and decomposition in queueing systems—A unified approachPerformance Evaluation, 1985
- Iterative Aggregation-Disaggregation Procedures for Discounted Semi-Markov Reward ProcessesOperations Research, 1985
- Product-Form Synthesis of Queueing NetworksIEEE Transactions on Software Engineering, 1985
- Bounds for the Positive Eigenvectors of Nonnegative Matrices and for their Approximations by DecompositionJournal of the ACM, 1984
- Markov Chain Models — Rarity and ExponentialityPublished by Springer Nature ,1979