slides: final touches to section 4
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@ -81,8 +81,8 @@ How to estimate sample median, mode and FWHM?
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. . .
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\centering
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\setbeamercovered{}
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\begin{center}
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\begin{tikzpicture}[remember picture, >=Stealth]
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% line
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\draw [line width=3, ->, cyclamen] (-5,0) -- (5,0);
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@ -107,43 +107,34 @@ How to estimate sample median, mode and FWHM?
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\node at (2.45,-0.7) (f1) {};
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\node at (2.45,0.7) (f2) {};
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\end{tikzpicture}
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\end{center}
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. . .
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\begin{center}
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\begin{tikzpicture}[remember picture, overlay]
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% region
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\draw [orange, fill=orange, opacity=0.5] (1a) rectangle (1b);
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\draw [gray, fill=gray, opacity=0.5] (1a) rectangle (1b);
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\end{tikzpicture}
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\end{center}
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. . .
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\begin{center}
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\begin{tikzpicture}[remember picture, overlay]
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% region
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\draw [orange, fill=orange, opacity=0.5] (2a) rectangle (1b);
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\draw [gray, fill=gray, opacity=0.6] (2a) rectangle (1b);
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\end{tikzpicture}
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\end{center}
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. . .
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\begin{center}
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\begin{tikzpicture}[remember picture, overlay]
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% region
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\draw [orange, fill=orange, opacity=0.5] (3a) rectangle (1b);
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\draw [gray, fill=gray, opacity=0.7] (3a) rectangle (1b);
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\end{tikzpicture}
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\end{center}
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. . .
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\begin{center}
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\begin{tikzpicture}[remember picture, overlay]
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% region
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\draw [ultra thick] (f1) -- (f2);
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\end{tikzpicture}
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\end{center}
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## Sample FWHM
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@ -166,7 +157,7 @@ $$
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G \left( \frac{x-x_i}{\varepsilon} \right)
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$$
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The parameter $\varepsilon$ controls the strength of the smoothing
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- The parameter $\varepsilon$ controls the strength of the smoothing
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:::
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::: {.column width=50%}
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@ -210,22 +201,21 @@ $$
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## Sample FWHM
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Silverman's rule of thumb [@silver86]:
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**Silverman's rule of thumb** [@silver86]:
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$$
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\varepsilon = 0.88 \, S_N
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\left( \frac{d + 2}{4}N \right)^{-1/(d + 4)}
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$$
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with:
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where:
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- $S_N$ is the sample standard deviation
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- $d$ is number of dimensions ($d = 1$)
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. . .
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Numerical minimization (Brent) for $\quad f_{\varepsilon_{\text{max}}}$
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Numerical root finding (Brent) for $\quad f_{\varepsilon}(x_{\pm}) =
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Minimization (Brent) for $\quad f_{\varepsilon_{\text{max}}}$
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Root finding (Brent-Dekker) for $\quad f_{\varepsilon}(x_{\pm}) =
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\frac{f_{\varepsilon_{\text{max}}}}{2}$
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