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\subsection{Gaussain Processes}  \label{sec:GaussinaProcesses}  In this paper we consider a specific class of regression functions $\mathcal{GP}$ -- Gaussian Processes. Any process $P\in\mathcal{GP}$ is uniqely defined by its mean $\mu(\mathbf{x}) = \mathrm{E}\left[f(\mathbf{x})\right]$ and covariance $\mathrm{Cov}\left(y, \[\mathrm{Cov}\left(y,  y^\prime\right) = k\left(\mathbf{x}, \mathbf{x}^\prime\right) = \mathrm{E}\left[\left(f\left(\mathbf{x}\right) - \mu\left(\mathbf{x}\right)\right) \left(f\left(\mathbf{x}^\prime\right) - \mu\left(\mathbf{x}^\prime\right)\right)\right]$ \mu\left(\mathbf{x}^\prime\right)\right)\right]\]  functions. If the mean function is set to zero, i.e. $\mu(\mathbf{x}) = \mathrm{E}\left[f\left(\mathbf{x}\right)\right] = 0$, and covariance function is assumed to be known, aposterior mean value of the Gaussian Process in the test set $X_*$ has form   \cite{Rasmussen} $\hat{f}(X_*) = K_* K^{-1} Y$, where $K_* = K(X_*, X) = \left[k(\mathbf{x}_i, \mathbf{x}_j), i = \overline{1, N_*}, j = \overline{1,N}\right]$ and $K = K(X, X) = \left[k(\mathbf{x}_i, \mathbf{x}_j), i, j = \overline{1, N}\right]$.