diff --git a/tex/chapters/mvg.tex b/tex/chapters/mvg.tex
index 94cad5f..ac7b1a9 100644
--- a/tex/chapters/mvg.tex
+++ b/tex/chapters/mvg.tex
@@ -16,7 +16,7 @@ where $\sigma$ is a smoothing parameter called standard deviation.
 
 %In the discrete case the Gaussian filter is easily computed with the sliding window algorithm in time domain.
 If the filter kernel is separable, the convolution is also separable i.e. multi-dimensional convolution can be computed as individual one-dimensional convolutions with a one-dimensional kernel.
-Because of $\operatorname{e}^{x^2+y^2} = \operatorname{e}^{x^2}\cdot\operatorname{e}^{y^2}$ the Gaussian filter is separable and can be easily applied to multi-dimensional signals. \todo{quelle}
+Because of $e^{x^2+y^2} = e^{x^2}\cdot e^{y^2}$ the Gaussian filter is separable and can be easily applied to multi-dimensional signals. \todo{quelle}
 
 % TODO ähnlichkeit Gauss und KDE -> schneller Gaus = schnelle KDE