ABSTRACT
The optimal variance estimate of Gaussian stochastic process based on discrete sample is equivalent to the error given by for the same sample size. Approaching the variance of optimal variance estimate of Gaussian stochastic process to zero is especially evident while passing from discrete to continuous observation of stochastic process. Depending on which errors are predominant in the analysis of errors, limitations arise due to insufficient knowledge about the correlation function or due to inaccurate measurement of realizations of the investigated stochastic process. The bias of variance estimate coincides with the variance of mathematical expectation estimate of stochastic process. Difference between the transformation characteristic and the square-law function can lead to high errors under definition of the stochastic process variance. The researcher defines the variance estimate on the basis of investigation of a single realization of stochastic process. The intrinsic amplifer channel noise samples are independent of each other.
