Overview

This text discusses stochastic models of systems analysis. It covers many aspects and different stages from the construction of mathematical models of real systems, through mathematical analysis of models based on simplification methods, to the interpretation of real stochastic systems. The stochastic models described here share the property that their evolutionary aspects develop under the influence of random factors. It has been assumed that the evolution takes place in a random medium, for instance unilateral interaction between the system and the medium. As only Markovian models of random medium are considered in this book, the stochastic models described here are determined by two processes, a switching process describing the evolution of the systems and a switching process describing the changes of the random medium.

Full Product Details

Author:   Vladimir S. Korolyuk ,  Vladimir V. Korolyuk
Publisher:   Springer
Imprint:   Springer
Edition:   1999 ed.
Volume:   469
Dimensions:   Width: 15.50cm , Height: 1.20cm , Length: 23.50cm
Weight:   1.330kg
ISBN:  

9780792356066

ISBN 10:   0792356063
Pages:   185
Publication Date:   28 February 1999
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   In Print  
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Table of Contents

1 Introduction.- 1.1 Classification and properties of stochastic systems.- 1.2 Renewal processes.- 2 Markov renewal processes.- 2.1 Definition of Markov renewal process.- 2.2 Semi-Markov processes.- 2.3 Ergodicity and stationary distribution.- 3 Phase merging algorithms.- 3.1 Reducible-invertible operators.- 3.2 Perturbation of reducible-invertible operators.- 3.3 Martingale characterization of Markov processes.- 3.4 Pattern limit theorem.- 3.5 Ergodic phase merging.- 3.6 Splitting phase merging.- 3.7 Heuristic phase merging principles.- 4 Evolutional stochastic system in a random medium.- 4.1 Stochastic additive functionals.- 4.2 Storage Processes.- 4.3 Random evolution.- 4.4 Ergodic average and diffusion approximation of random evolutions.- 4.5 Splitting average and diffusion approximation of random evolution.- 4.6 Application of average and diffusion approximation algorithms.- 4.7 Counting processes.- 4.8 Proofs of limit theorems.- 5 Diffusion approximation of Markov queueing systems and networks.- 5.1 Algorithms of diffusion approximation.- 5.2 Markov queueing processes.- 5.3 Average and diffusion approximation.- 5.4 Stationary distribution.- 5.5 Markovian queueing systems.- 5.6 Markovian queueing networks.- References.

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