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Kasım BARIKTABASOV | Çinara CUMABAYEVA | Ulan BRİMKULOV
Many examinations with thousands of participating students are organized world-wide every year. Usually, this large number of students sit the exams simulta-neously and answer almost the same set of questions. This method of learning assessment requires tremendous effort and resources to prepare the venues, print question books and organize the whole process. Additional restrictions and obsta-cles may appear in conditions similar to those during the COVID-19 pandemic. One way to obviate the necessity of having all the students take an exam during the same period of time is to use a computer-as ...More
Ulan BRİMKULOV | Çinara CUMABAYEVA | Kasım BARIKTABASOV
Many computational algorithms related to Markov processes contain the covariance matrix of measurements. Approximation of covariance matrix of measurements of observed random process by the covariance matrix of Markov process is of great interest. Because it gives an opportunity to develop computationally efficient algorithms for analysis of Markov processes (parametric identification, filtering, interpolation and others).
This paper presents the algorithm of approximation of the covariance matrix of the observed process by the covariance matrix of a multiply connected (m-connected) Markov p ...More
The article discusses the matrices of the form A(n)(1), A(n)(m), A(N)(m), whose inverses are: tridiagonal matrix A(n)(-1) (n - dimension of the A(N)(-m) matrix), banded matrix A(n)(-m) (m is the half-width band of the matrix) or block-tridiagonal matrix A(N)(-m) (N = n x m - full dimension of the block matrix; m - the dimension of the blocks) and their relationships with the covariance matrices of measurements with ordinary (simple) Markov Random Processes (MRP), multiconnected MRP and vector MRP, respectively. Such covariance matrices frequently occur in the problems of optimal filtering, ext ...More