The article under the heading «Estimation of statistical characteristics of the mathematical model by Pugachev's method» is devoted to the problem of taking into account the error of the initial data. The essence of the problem is the need to obtain statistical estimates of probabilistic characteristics efficiently with a given accuracy, and also with minimal losses of computer time.
The article consists of an introduction, two sections, an opinion, a list of references.
The first section presents the current state of the investigated problem, describes methods of mathematical modeling and an algorithm for solving it.
The second section is devoted to the development of a software product, describes the used tools, the structure of the software product, user classes and methods. The authors demonstrate the interface of the application and its features, as well as the results of testing, which was conducted on a set of statistical data obtained during a multifactor unstable process.
The authors point out the advantages and disadvantages of Pugachev's method. Evaluating the work as a whole, it can be argued that the practical importance and prospects of the development of this direction of research is obvious.
The article presents an algorithm for restoration of the original data table using GRNN artificial neural network and the results of the algorithm in testing and empirical data. The given article also deals with calculations of the relative error for different data types with different percentages of passes. The quality of the analyzed data resulting from the passive experiment, as well as the reliability of the analysis results depends on one of the most important factors: the presence of these missing values. The distortion of the original data or incompleteness may distort the result in the general modeling process. Gaps in the original data table may be associated with a complete lack of data (raw data incompleteness) and contradictions arising from the data. And this kind of problem can occur not only with the values of a single attribute, but also with the values of a certain set of attributes especially in those cases when it comes to the large dimension of the factor space.
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