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Undifferentiated optimization of data sample

Obtaining an unbiased data sample is an important task in the statistical analysis of experimental data. The unbiased data sample is a representative data sample. The natural desire is to obtain a representative data sample using computational methods. A procedure for adjusting the structure of the data sample in line with the structure of statistical population is called “correction of a data sample”. This procedure optimizes data sample, minimizing the difference between a theoretical distribution of control variables and an empirical distribution of control variables. The variables are called control ones if we know the distribution of their spectral values in the statistical population. All of the known methods of adjusting the data sample have significant drawback, as they “correct” an empirical distribution function, but not the data sample. For example, that refers to IPF algorithm [1], [2]. We discuss an algorithm that corrects sample data rather than their empirical distributions. This algorithm is randomized. An algorithm is called randomized, if the execution of one or several iterations relies on a random rule [3]. The optimization of a data sample carried out with a randomized algorithm cannot be differentiable. This algorithm can be considered as the inhomogeneous Markov chain [4].