added todos to all chapters
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@@ -44,6 +44,7 @@ However, reducing the sample size by distributing the data on a equidistant grid
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Silverman \cite{silverman1982algorithm} originally suggested to combine adjacent data points into data bins, which results in a discrete convolution structure of the KDE.
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Allowing to efficiently compute the estimate using a FFT algorithm.
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This approximation scheme was later called binned KDE (BKDE) and was extensively studied \cite{fan1994fast} \cite{wand1994fast} \cite{hall1996accuracy} \cite{holmstrom2000accuracy}.
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While the FFT algorithm poses an efficient algorithm for large sample sets, it adds an noticeable overhead for smaller ones.
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The idea to approximate a Gaussian filter using several box filters was first formulated by Wells \cite{wells1986efficient}.
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Kovesi \cite{kovesi2010fast} suggested to use two box filters with different widths to increase accuracy maintaining the same complexity.
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