small lit fix

tex/chapters/mvg.tex

@@ -5,7 +5,7 @@
 % Repetitive Box filter to approx Gauss
 % Simple multipass, n/m approach, extended box filter
 Digital filters are implemented by convolving the input signal with a filter kernel, i.e. the digital filter's impulse response.
-Consequently, the filter kernel of a Gaussian filter is a Gaussian with finite support \cite[120]{dspGuide1997}.
+Consequently, the filter kernel of a Gaussian filter is a Gaussian with finite support \cite{dspGuide1997}.
 Assuming a finite-support Gaussian filter kernel of size $M$ and a input signal $x$, discrete convolution produces the smoothed output signal 
 \begin{equation}
 \label{eq:gausFilt}

tex/chapters/relatedwork.tex

@@ -42,7 +42,7 @@ In general, it is desirable to omit a grid, as the data points do not necessary
 However, reducing the sample size by distributing the data on a equidistant grid can significantly reduce the computation time, if an approximative KDE is acceptable.
 Silverman \cite{silverman1982algorithm} originally suggested to combine adjacent data points into data bins, which results in a discrete convolution structure of the KDE.
 Allowing to efficiently compute the estimate using a FFT algorithm.
-This approximation scheme was later called binned KDE (BKDE) and was extensively studied \cite{fan1994fast} \cite{wand1994fast} \cite{hall1996accuracy} \cite{holmstrom2000accuracy}.
+This approximation scheme was later called binned KDE (BKDE) and was extensively studied \cite{fan1994fast} \cite{wand1994fast} \cite{hall1996accuracy}.
 While the FFT algorithm poses an efficient algorithm for large sample sets, it adds an noticeable overhead for smaller ones.
 
 The idea to approximate a Gaussian filter using several box filters was first formulated by Wells \cite{wells1986efficient}.