Refinable Bounds for Large Markov Chains
IEEE Transactions on Computers
An Algorithmic Approach to Stochastic Bounds
Performance Evaluation of Complex Systems: Techniques and Tools, Performance 2002, Tutorial Lectures
A Matrix Pattern Compliant Strong Stochastic Bound
SAINT-W '05 Proceedings of the 2005 Symposium on Applications and the Internet Workshops
Google's PageRank and Beyond: The Science of Search Engine Rankings
Google's PageRank and Beyond: The Science of Search Engine Rankings
A Toolbox for Component-Wise Bounds for Steady-State Distribution of a DTMC
QEST '10 Proceedings of the 2010 Seventh International Conference on the Quantitative Evaluation of Systems
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We present several new improvements for a recently published algorithm [5] for computing the steady-state distribution of a finite ergodic Markov chain, which has a proved monotone convergence under some structural constraints on the matrix. We show how to accommodate infinite state space and that the structural constraints of the algorithm are consistent with Pagerank matrix. We present how to combine this algorithm with stochastic comparison theory to numerically obtain bounds and we prove a pre-processing of the matrix which allows to alleviate the structural constraints. The approaches are illustrated through several small examples.