diff --git a/electives/others/amr/parts/02_Sensors-Actuators/03_cameras.tex b/electives/others/amr/parts/02_Sensors-Actuators/03_cameras.tex
new file mode 100644
index 0000000..adbf8da
--- /dev/null
+++ b/electives/others/amr/parts/02_Sensors-Actuators/03_cameras.tex
@@ -0,0 +1,9 @@
+\shortdefinition[Pinhole projection]
+$\begin{bmatrix}
+        u \\ v
+    \end{bmatrix}
+    = \frac{f}{z}
+    \begin{bmatrix}
+        x \\ y
+    \end{bmatrix}$
+    with $f$ the distance to the lens and $z$ the full distance
diff --git a/semester4/ps/ps-jh/parts/03_expected-value/00_cont.tex b/semester4/ps/ps-jh/parts/03_expected-value/00_cont.tex
new file mode 100644
index 0000000..ec9f7a2
--- /dev/null
+++ b/semester4/ps/ps-jh/parts/03_expected-value/00_cont.tex
@@ -0,0 +1,13 @@
+\subsection{Allgemeiner Erwartungswert}
+\shortdefinition Für $\cX : \Omega \rightarrow \R_+$, $\E[\cX] = \int_{0}^{\8} (1 - F_\cX(x)) \dx x$
+
+\shortremark $\E[\cX]$ immer definiert und endlich oder unendlich
+
+\shorttheorem $\cX$ n.-neg. Dann: $\E[\cX] \geq 0$. $=$, wenn $\cX = 0$ fast sicher
+
+\shortdefinition $\E[\cX] = \E[\cX_+] - \E[\cX_-]$ mit $\cX_-$ auch n.-neg.
+
+\shortremark $|\cX| = \cX_+ + \cX_-$. Für $\cX \geq 0$ ist $\E[\cX]$ immer definiert.
+Falls $\cX$ kein konst. Vorzeichen, $\E[\cX]$ undef.
+
+\shortremark $\E[\cX] = \int_{0}^{\8} (1 - F_\cX(x)) \dx x - \int_{-\8}^{0} F_\cX(x)$
diff --git a/semester4/ps/ps-jh/parts/03_expected-value/01_disc.tex b/semester4/ps/ps-jh/parts/03_expected-value/01_disc.tex
new file mode 100644
index 0000000..5b302eb
--- /dev/null
+++ b/semester4/ps/ps-jh/parts/03_expected-value/01_disc.tex
@@ -0,0 +1,14 @@
+\subsection{Diskrete Zufallsvariablen}
+\shorttheorem Für $\cX$ mit Werten fast sicher in $W$:
+\[
+    \E[\cX] = \sum_{x \in W} x \cdot \P[\cX = x] = \sum_{x \in W} x \cdot p_\cX(x)
+\]
+
+\shortremark $\E[\cX]$ wohldefiniert falls $(x \cdot p_\cX(x))_{x \in W}$ abs. konv.
+
+\subsubsection{Beispiele}
+\begin{itemize}
+    \item $\cX \sim \text{Ber}(p)$: $\E[\cX] = p$
+    \item $\cX \sim \text{Bin}(n, p)$: $\E[\cX] = np$
+    \item $\cX \sim \text{Poisson}(\lambda)$: $\E[\cX] = \lambda$
+\end{itemize}
diff --git a/semester4/ps/ps-jh/probability-and-statistics-cheatsheet.pdf b/semester4/ps/ps-jh/probability-and-statistics-cheatsheet.pdf
index 120ee4d..b6bc250 100644
Binary files a/semester4/ps/ps-jh/probability-and-statistics-cheatsheet.pdf and b/semester4/ps/ps-jh/probability-and-statistics-cheatsheet.pdf differ
diff --git a/semester4/ps/ps-jh/probability-and-statistics-cheatsheet.tex b/semester4/ps/ps-jh/probability-and-statistics-cheatsheet.tex
index 6ec4a5b..3d1ec60 100644
--- a/semester4/ps/ps-jh/probability-and-statistics-cheatsheet.tex
+++ b/semester4/ps/ps-jh/probability-and-statistics-cheatsheet.tex
@@ -59,5 +59,11 @@
 \input{parts/02_discrete-continuous-rv/05_cont-distributions/03_normal.tex}
 % \input{parts/02_discrete-continuous-rv/}
 
+\newsectionNoPB
+\section{Erwartungswert}
+\input{parts/03_expected-value/00_cont.tex}
+\input{parts/03_expected-value/01_disc.tex}
+% \input{parts/03_expected-value/}
+
 
 \end{document}