this is for holding javascript data
Pavel Erofeev edited GP.tex
over 9 years ago
Commit id: 025336f8f73096ade35a328de9b05d775a5768c6
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In that case observations $y(\mathbf{x})$ are generated by Gaussian Process with zero mean and covariance function $\mathrm{Cov}\left(y(\mathbf{x}), y(\mathbf{x}^\prime)\right) = k(\mathbf{x}, \mathbf{x}^\prime) + \tilde{\sigma}^2\delta(\mathbf{x}- \mathbf{x}^\prime)$, where $\delta(\mathbf{x})$ is a Dirac delta funciton.
Thus, aposterior mean funciton of Gaussian Process $f(\mathbf{x})$ in the points of test set $X_*$ takes form:
\begin{equation}%\label{meanNoise} \begin{equation}
\label{meanNoise}
\hat{f}(X_*) = K_* \left(K + \tilde{\sigma}^2 I\right)^{-1} Y,
\end{equation}
where $I$ -- identity matrix of size $(N \times N)$.