Titlebook: Linear Algebra, Markov Chains, and Queueing Models; Carl D. Meyer,Robert J. Plemmons Conference proceedings 1993 Springer-Verlag New York,

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Iterative Methods for Finding the Stationary Vector for Markov Chainsoblem, but since the eigenvalue is known, it can equally well be studied as a nullspace problem or as a linear system. We discuss iterative methods for each of these three formulations. Many of the applications, such as queuing modeling, have special structure that can be exploited computationally,

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Means and Variances in Markov Reward Systemsrd within this time period, as well as its variance. Though the emphasis is on discrete time Markov processes, continuous-time reward processes will also be considered. For the discrete time reward process, the determination of the expected reward form 0 to . is of course trivial. It is of interest,

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A Direct Algorithm for Computing the Stationary Distribution of a ,-Cyclic Markov Chain to adapt Gaussain elimination to exploit this structure. When there are . blocks of approximately equal size, the savings in computational effort from exploiting this structure is a factor of about 3/...

柳樹；枯黃

Approximate Analysis of a Discrete-Time Queueing Model of the Shared Buffer ATM Switchnd it is modelled by an Interrupted Bernoulli Process. The discrete-time queueing system is analyzed approximately. It is first decomposed into subsystems, and then each subsystem is analyzed separately. The results from the subsystems are combined together through an iterative scheme. The analysis

白楊魚

Algorithms for Infinite Markov Chains with Repeating Columnsqueue, then the stationary probability vector is matrix-geometric. The major difficulty in computing the stationary vector resides in the determination of the solution of a non-linear matrix equation..Various algorithms may be used for that purpose. They are discussed, and their computational effici

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CRAY-2 Memory Organization and Interprocessor Memory Contentionexecute together on such a system, each program’s performance, relative to its performance on a single dedicated processor, degrades because of contention among processors for shared memory. From the results of the simulation study, the paper proposes analytic forms for the asymptotic steady state b

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Algorithms for Periodic Markov Chainsntage of the periodic structure, both on the complete state space and on a reduced level. On area of particular interest occurs when the model is both periodic and nearly-completely- decomposable. In this case we show how iterative aggregation/disaggregation algorithms may be modified to take advantage of the periodicity property.

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Algorithms for Infinite Markov Chains with Repeating Columnsn of the solution of a non-linear matrix equation..Various algorithms may be used for that purpose. They are discussed, and their computational efficiency is compared. While most of the algorithms examined are well known, some are presented here for the first time.