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Features and Application of Stochastic Analytical Modeling
Abstract
The chapter deals with the main features of stochastic modeling and its application in the investigation of probabilistic processes in computer processing. At the beginning, a brief overview of the possibilities offered by the probabilistic analytical modeling was made with the definition of basic characteristics and short comments. The relationship of discrete and continuous random variables to stochastic modeling is discussed in the first part, focusing on Markov processes with application examples presented. The second part is devoted to the stochastic analytical approximation. The third part is directed to the discussion of the stochastic modeling by means of the theory of Markov processes, concentrating on random processes with discrete states and discrete time (Markov Chain) and on analogous processes, but with continuous time. Each of these two sub-parts is illustrated with numerous examples of sample computer processing situations with analysis of the results and appropriate tabular and graphical illustration of the resulting estimates.
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