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[PS] Joint continuous distribution start
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\[
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\[
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\P[\cX_1 \! \leq \! a_1, \ldots, \cX_n \! \leq \! a_n]\! = \! \int_{-\8}^{a_1}\! \ldots \! \int_{-\8}^{a_n} \! f(x_1, \ldots) \dx x_n \ldots \mbox{d} x_1
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\P[\cX_1 \! \leq \! a_1, \ldots, \cX_n \! \leq \! a_n]\! = \! \int_{-\8}^{a_1}\! \ldots \! \int_{-\8}^{a_n} \! f(x_1, \ldots) \dx x_n \ldots \mbox{d} x_1
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\]
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\]
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\shorttheorem $f$ gem. Dichte
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\[
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\int_{-\8}^{\8}\! \ldots \! \int_{-\8}^{\8} \! f(x_1, \ldots) \dx x_n \ldots \mbox{d} x_1 = 1
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\]
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Jeder Funk. $f$ die obiges erfüllt ein W-Raum $(\Omega, \cF, \P)$ und $n$-Z.V. $\cX_i$ zugeo. werden, s.d. $f$ die gem. Dichte von $\cX_i$ ist.
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\inlineintuition $f(x_1, \ldots) \dx x_1, \ldots$ beschreibt die W., dass ein zu-fälliger Punkt $(\cX_1, \ldots)$ in $[x_1, x_1 + \dx x_1] \times \ldots$ liegt
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\shortexample Gleichverteilungen:
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\begin{itemize}
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\item \bi{Einheistquadrat} $f(x, y) = \begin{cases}
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1 & (x, y) \in [0, 1]^2 \\
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0 & \text{sonst}
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\end{cases}$
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\item \bi{Einheistkreisscheibe} $D = \{ (x, y) \in \R^2 \divider x^2 + y^2 \leq 1 \}$
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$f(x, y) = \begin{cases}
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\frac{1}{\pi} & (x, y) \in D \\
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0 & \text{sonst} \\
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\end{cases}$
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\end{itemize}
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\shorttheorem[Erwartungswert Bild]
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\[
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\E[\varphi(\cX_1, \ldots)] \! = \! \int_{-\8}^{\8} \ldots \int_{-\8}^{\8} \varphi(x_1, \ldots) f(x_1, \ldots) \dx x_n \ldots
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\]
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\shortdefinition[Randverteilung] Falls $\cX, \cY$ gemeinsame Verteilung $F$ haben, so ist $F_\cX : \R \rightarrow [0, 1]$ gegeben durch:
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\[
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x \mapsto F_\cX(x) \! = \! \P[\cX \leq x] \! = \! \P[\cX \leq x, \cY < \8] \! = \! \limit{y}{\8} F(x, y)
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\]
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