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[PS] ch7
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\subsection{Allgemeiner Erwartungswert}
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\definition \textbf{Erwartungswert} (nicht-negativ)
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$$
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\E[X] = \int_{0}^{\infty} \biggl( 1-F_X(x) \biggr)\ dx
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@@ -11,59 +13,36 @@ $$
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\theorem $\forall \omega \in \Omega: X(\omega) \geq 0 \implies \E[X] \geq 0$\\
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\subtext{Gleichheit: $\E[X] = 0 \iff X=0$, fast sicher}
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\definition \textbf{Erwartungswert}
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$$
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\E[X] = \E[X_+] - \E[X_-] \quad\text{falls}\quad \E[|X|]<\infty
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$$
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{\scriptsize
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\remark $X$ kein konst. Vorzeichen, nicht $\E[|X|] < \infty $: $\E[X]$ undefiniert
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}
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% Sollte nicht relevant sein
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\subsection{Diskreter Erwartungswert}
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% \definition \textbf{Erwartungswert}
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% $$
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% \E[X] = \E[X_+] - \E[X_-] \quad\text{falls}\quad \E[|X|]<\infty
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% $$
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% {\scriptsize
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% \remark $X$ kein konst. Vorzeichen, nicht $\E[|X|] < \infty $: $\E[X]$ undefiniert
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% }
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\theorem \textbf{Diskreter Erwartungswert}
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\theorem \textbf{Diskreter Erwartungswert}\\
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\subtext{$X: \Omega \to \R,\quad W \cleq \N,\quad \phi: \R \to \R$}
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$$
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\E\bigl[ \phi(X) \bigr] = \sum_{w \in W} \phi(x) \cdot \P[X = x]
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$$
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\subtext{$X: \Omega \to \R,\quad W \cleq \N,\quad \phi: \R \to \R$}
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\begin{center}
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\begin{tabular}{l|l|l}
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$\text{Ber}(p)$ & $\E[X] = p$ & $\V[X] = p(1-p)$\\
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$\text{Poisson}(\lambda)$ & $\E[X] = \lambda$ & $\V[X] = \lambda$\\
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$\text{Bin}(n,p)$ & $\E[X] = n\cdot p$ & $\V[X] = np(1-p)$\\
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$\mathbb{I}_A$ & $\E[\mathbb{I}_A] = \P[A]$ \\
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$\exp(\lambda)$ & $\E[X] = \frac{1}{\lambda}$ \\
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$\mathcal{U}([a,b])$ & $\E[X] = \frac{a+b}{2}$
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\end{tabular}
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\end{center}
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\newpage
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\subsection{Stetiger Erwartungswert}
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\definition \textbf{Erwartungswert} (stetig)
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\definition \textbf{Erwartungswert} (stetig)\\
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\subtext{$X: \Omega \to \R,\quad f(x) \text{ Dichtefunktion}$}
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$$
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\E[X] = \int_{-\infty}^{\infty} x \cdot f(x)\ dx
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$$
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\subtext{$X: \Omega \to \R,\quad f(x) \text{ Dichtefunktion}$}
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\theorem \textbf{Linearität}
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\begin{align*}
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\text{(i)} &\quad \E[\lambda X] &=& \lambda \E[X] \\
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\text{(ii)} &\quad \E[X + Y] &=& \E[X] + \E[Y]
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\end{align*}
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\theorem \textbf{Eigenschaften}\\
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\subtext{$X,Y:\Omega\to\R,\quad\lambda\in\R$}
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\theorem \textbf{Monotonie}
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$$
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X \leq Y \implies \E[X] \leq \E[Y]
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$$
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\theorem \textbf{Multiplikation}\\
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\smalltext{$X,Y$ unabhängig}
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$$
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\E[X \cdot Y] = \E[X]\cdot\E[Y]
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$$
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\begin{align*}
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\text{(i)} &\quad \E[\lambda X] &=& \lambda \E[X] \\
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\text{(ii)} &\quad \E[X + Y] &=& \E[X] + \E[Y] \\
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\text{(iii)} &\quad X \leq Y &\implies& \E[X] \leq \E[Y] \\
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\text{(iv)} &\quad X \perp Y &\implies& \E[X\cdot Y] = \E[X]\cdot\E[Y]
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\end{align*}
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\theorem \textbf{Dichtefunktion bei Abbildungen}\\
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\smalltext{$\phi:\R\to\R$ stückweise stetig, beschränkt}
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@@ -79,36 +58,6 @@ $$
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$$
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\subtext{Auch verallgemeinert für $X_1,\ldots,X_n$, $\phi_1,\ldots,\phi_n$}
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\newpage
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\subsection{Ungleichungen}
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\theorem \textbf{Markov}\\
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\smalltext{$X \geq 0,\quad g: X(\Omega)\to[0,\infty)$ wachsend}
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$$
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\forall c \in \R \text{ s.d. } g(c)>0:\qquad \P[X \geq c] \leq \frac{\E[g(X)]}{g(c)}
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$$
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\theorem \textbf{Jensen}\\
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\smalltext{$\phi:\R\to\R$ konvex,$\quad \E[\phi(X)],\E[X]$ wohldefiniert}
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$$
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\phi\Bigl(\E[X]\Bigr)\leq\E\Bigl[\phi(X)\Bigr]
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$$
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\lemma \textbf{Dreiecksungleichung}\\
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\subtext{Jensen mit $\phi(X) = |X|$ und $\phi(X) = X^2$}
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$$
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\Bigl\vert\E[X]\Bigr\vert\leq\E\Bigl[\vert X\vert\Bigr] \qquad \E[|X|] \leq \sqrt{\E[X^2]}
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$$
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\theorem \textbf{Chebychev}\\
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\subtext{$Y$ s.d. $\V[Y] < \infty,\quad c>0$}
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$$
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\P\Bigl[ |Y-\E[Y]| \geq c \Bigr] \leq \frac{\V[Y]}{c^2}
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$$
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\theorem \textbf{Chernoff}
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% Slides, ende von ZGS
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\newpage
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\subsection{Varianz}
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