In this paper discusses application of the stochastic simulation method, as a method of alternative identification of the function of $f_{qz}(g_{z})$ random variable $Q_z$ - shift production and function $f_{qd}(g_{d})$ random variable $Q_d$ - daily production. The algorithms using random number generators were presented. It was noted that the use of the stochastic simulation method, could be more effective in resolving these issues, in some cases, when compared to the analytical method.
This paper contain consideration about described the possibility of reduction of stochastic models by usage functional relationships, which can occur between random variables in model. Functional relations were also described as outcome schedules let schedule beta, chi-square, Cauchy, F-Snedecor, gamma and uniform. In this way the conducted reduction of model has profitable influence on his further usage, and so simplifies the record in figure of computer programme, and then the process of stochastic simulation is shortened.
This paper contain consideration about described the possibility of reduction of stochastic models by usage functional relationships, which can occur between random variables in model. Functional relations were also described, which as outcome schedule gave normal distribution, log-normal distribution, t-Student distribution and exponential distribution. In first part of publication, the outcome distribution were: beta, chi-square, Cauchy, F-Snedecor, gamma and uniform. The usage of described functions of random variables in stochastic model, can have the profitable influence on its further utilization, through the simplification of its record in figure of computer programme and the shortening of the stochastic simulation process.
