diff --git a/semester4/ps/ps-jh/parts/05_limit-theorems/00_intro.tex b/semester4/ps/ps-jh/parts/05_limit-theorems/00_intro.tex
index d2ced9d..5ba5ec2 100644
--- a/semester4/ps/ps-jh/parts/05_limit-theorems/00_intro.tex
+++ b/semester4/ps/ps-jh/parts/05_limit-theorems/00_intro.tex
@@ -3,10 +3,3 @@
 {\scriptsize \shortterm In Zusammenhang mit Zufallsvariablen auch \bi{Sichprobenmittel} genannt. Realisierung wird \bi{empirischer Mittelwert} genannt}
 
 % TODO: Possibly add the remark from P288 (P6 in Slide Deck 6) on expected value
-
-\shorttheorem[Schwaches Ges. der grossen Zahlen] Sei $K = \{ 1, 2, \ldots \}$ und $\forall k \in K : \cX_k$ unabh. Z.V. mit $\E[\cX_k] = \mu$; $\V[\cX_k] = \sigma^2$:
-\[
-    \overline{\cX}_n = \frac{1}{n} S_n = \frac{1}{n} \sum_{k = 1}^{n} \cX_k
-\]
-Dann konvergiert $\overline{\cX}_n$ für $n \rightarrow \8$ in Wahrscheinlichkeit gegen $\mu = \E[\cX_k]$,
-also $\forall \varepsilon > 0$ gilt $\P[|\overline{\cX}_n - \mu| > \varepsilon] \overset{n \rightarrow \8}{\longrightarrow} 0$
diff --git a/semester4/ps/ps-jh/parts/05_limit-theorems/01_weak-law-of-large-numbers.tex b/semester4/ps/ps-jh/parts/05_limit-theorems/01_weak-law-of-large-numbers.tex
new file mode 100644
index 0000000..e69de29
diff --git a/semester4/ps/ps-jh/parts/05_limit-theorems/02_strong-law-of-large-numbers.tex b/semester4/ps/ps-jh/parts/05_limit-theorems/02_strong-law-of-large-numbers.tex
new file mode 100644
index 0000000..e69de29
diff --git a/semester4/ps/ps-jh/parts/05_limit-theorems/03_central-limit-theorem.tex b/semester4/ps/ps-jh/parts/05_limit-theorems/03_central-limit-theorem.tex
new file mode 100644
index 0000000..ab93e25
--- /dev/null
+++ b/semester4/ps/ps-jh/parts/05_limit-theorems/03_central-limit-theorem.tex
@@ -0,0 +1,19 @@
+\subsection{Zentraler Grenzwertsatz}
+\shortdefinition[Konvergenz in Verteilung] $(\cX_n)_{n \in \N}$, $\cX$ mit V.F.\\
+$(F_n)_{n \in \N}$, $F$.
+$(\cX_n)_{n \in \N}$ \bi{konvergiert in V.} gegen $\cX$
+($\cX_n \overset{d}{\rightarrow} \cX$ für $n \rightarrow \8$), falls $\forall$ Stetigkeitsp. $x \in \R$ von $F$ gilt:
+\[
+    \limit{n}{\8} F_n(x) = \limit{n}{\8} \P[\cX_n \leq x] = \P[\cX \leq x] = F(x)
+\]
+
+\shorttheorem[Zentraler Grenzwertsatz (\textbf{ZGS})]\\
+{\scriptsize i.i.d = independent and identically distributed (u.i.v in DE)}
+
+$\cX_k$ i.i.d mit $\E[\cX_k] = \mu$, $\V[\cX_k] = \sigma^2$. Für Partialsummen $S_n = \sum_{k = 1}^{n} \cX_k$ gilt $\forall x \in \R$ (mit $\Phi$ V.F. von Std.-Norm.-V):
+\[
+    \limit{n}{\8} \P \left[ \frac{S_n - n\mu}{\sigma \sqrt{n}} \leq x \right] = \Phi(x)
+\]
+
+\shortremark $\E[S_n] = n \mu$, $\V[S_n] = n \sigma^2$; $S_n^* = \frac{S_n - \mu}{\sigma \sqrt{n}} \overset{\text{approx}}{\sim} \cN(0, 1)$
+für grosse $n$, mit $\overset{\text{approx}}{\sim}$ gespr. ``approx. gleichverteilt gemäss''
diff --git a/semester4/ps/ps-jh/probability-and-statistics-cheatsheet.pdf b/semester4/ps/ps-jh/probability-and-statistics-cheatsheet.pdf
index 6e2af89..0861855 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 c86bf6f..7bce7e9 100644
--- a/semester4/ps/ps-jh/probability-and-statistics-cheatsheet.tex
+++ b/semester4/ps/ps-jh/probability-and-statistics-cheatsheet.tex
@@ -84,6 +84,9 @@
 \newsection
 \section{Das Gesetz der grossen Zahlen}
 \input{parts/05_limit-theorems/00_intro.tex}
+\input{parts/05_limit-theorems/01_weak-law-of-large-numbers.tex}
+\input{parts/05_limit-theorems/02_strong-law-of-large-numbers.tex}
+\input{parts/05_limit-theorems/03_central-limit-theorem.tex}
 % \input{parts/05_limit-theorems/}