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Markus Bullmann
2018-02-20 10:43:34 +01:00
parent 6ffa7d1c15
commit e49c7a1cbf
5 changed files with 114 additions and 118 deletions
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tex/chapters/kde.tex
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@@ -1,4 +1,4 @@
\section{Binned Kernel Density Estimation}
\section{Kernel Density Estimation}
% KDE by rosenblatt and parzen
% general KDE
% Gauss Kernel
@@ -121,9 +121,6 @@ In terms of DSP this is analogous to filter the binned data with a Gaussian filt
This finding allows to speedup the computation of the density estimate by using a fast approximation scheme based on iterated box filters.
\commentByToni{hier vielleicht nochmal explizit erwähnen, also mit Namen, das der Gauss jetzt die BKDE approximiert und das diese erkenntniss toll und wichtig ist, weil wir so ein komplexes problem total einfach und schnell dargestellt haben. \commentByMarkus{Reicht das so?}}
%Given $n$ independently observed realizations of the observation set $X=(x_1,\dots,x_n)$, the kernel density estimate $\hat{f}_n$ of the density function $f$ of the underlying distribution is given with