On some mathematical and programming methods for construction of structural models of information flows

The flows of events in the modern information systems are not regular; so, the methods of analysis based on the classical theorems that are correct only under certain regularity conditions can lead to false conclusions including underestimation of risks of extreme events. The key problem of practical modeling and analysis of nonstationary information flows is selection of statistical methods for estimation of the unknown model parameters. For these purposes, the so-called method of moving the separation of mixtures based on a special decomposition of the original sample into subsamples (windows) and data analysis for each window within the framework of the mixed probability models is traditionally used by the members of Prof. V. Yu. Korolev's Scientific School. The paper describes the methods of stochastic data analysis based on the mixed probability models that can enhance the effectiveness of complex information systems research. The development and application of the proposed methods can be useful for the appropriate areas of applied mathematics and computer sciences.