diff --git a/entropy.tex b/entropy.tex
index 4f4ca91..7ef7fce 100644
--- a/entropy.tex
+++ b/entropy.tex
@@ -9,6 +9,7 @@
 %\usepackage{csquotes} % Recommended for biblatex
 \usepackage{tikz}
 \usepackage{pgfplots}
+\usetikzlibrary{positioning}
 \usepackage{float}
 \usepackage{amsmath}
 \PassOptionsToPackage{hyphens}{url}
@@ -51,7 +52,7 @@ S = -k_B \sum_i p_i \ln(p_i)
 It gives statistical meaning to the macroscopic phenomenon of classical thermodynamics 
 by defining the entropy $S$ of a macrostate as 
 the result of probabilities $p_i$ of all its constituting micro states.
-$k_B$ refers to the Boltzmann constant, which he himself did not determine.
+$k_B$ refers to the Boltzmann constant, which he himself did not determine but is part of todays SI system.
 
 \section{Shannon's axioms}
 \textit{Claude Shannon} adapted the concept of entropy to information theory.
@@ -72,6 +73,7 @@ and media for transmission and storage.
 In information theory, entropy can be understood as the expected information of a message.
 \begin{equation}
     H = E(I) = - \sum_i p_i \log_2(p_i)
+    \label{eq:entropy-information}
 \end{equation}
 This leaves $ I =log(1/p_i) = - log_2(p_i)$, implying that an unexpected message (low probability) carries
 more information than one with higher probability.
@@ -86,7 +88,9 @@ Because we attach high surprise only to the unlikely message of an eruption, the
 carries less information - we already expected it before it arrived.
 
 Putting the axioms and our intuitive understanding of information and uncertainty together,
-\autoref{fig:graph-entropy}
+we can see the logarithmic decay of information transported by a message as its probability increases in \autoref{fig:graph-information},
+as well as the entropy for a 2-event source given by solving \autoref{eq:entropy-information} for $i=2$, resulting in
+$-p * \log_2(p) - (1-p) * \log_2(1-p) $.
 
 \begin{figure}[H]
 \begin{minipage}{.5\textwidth}
@@ -96,7 +100,7 @@ Putting the axioms and our intuitive understanding of information and uncertaint
     samples=100,
     axis lines=middle,
     xlabel={$p$},
-    ylabel={Information},
+      ylabel={Information [bits]},
     xmin=0, xmax=1,
     ymin=0, ymax=6.1,
     grid=both,
@@ -118,9 +122,10 @@ Putting the axioms and our intuitive understanding of information and uncertaint
     samples=100,
     axis lines=middle,
     xlabel={$p$},
-    ylabel={Entropy},
+    ylabel={Entropy [bits]},
     xmin=0, xmax=1,
     ymin=0, ymax=1.1,
+    xtick={0,0.25,0.5,0.75,1},
     grid=both,
     width=8cm,
     height=6cm,
@@ -211,6 +216,35 @@ In a supervised training example, the cross entropy loss degenerates to $-\log(p
 true label vector is assumed to be the unit vector $e_i$ (one-hot).
 
 \subsection{Coding}
+The concept of entropy also plays a crucial role in the design and evaluation of codes used for data compression and transmission.
+In this context, \textit{coding} refers to the representation of symbols or messages
+from a source using a finite set of codewords.
+Each codeword is typically composed of a sequence of bits,
+and the design goal is to minimize the average length of these codewords while maintaining unique decodability.
+
+According to Shannon's source coding theorem, the theoretical lower bound for the average codeword length of a source
+is given by its entropy $H$.
+In other words, no lossless coding scheme can achieve an average length smaller than the source entropy when expressed in bits.
+Codes that approach this bound are called \textit{efficient} or \textit{entropy-optimal}.
+A familiar example of such a scheme is \textit{Huffman coding},
+which assigns shorter codewords to more probable symbols and longer ones to less probable symbols,
+resulting in a prefix-free code with minimal expected length.
+
+Beyond compression, coding is essential for reliable communication over imperfect channels.
+In real-world systems, transmitted bits are often corrupted by noise, requiring mechanisms to detect and correct errors.
+One simple but powerful concept to quantify the robustness of a code is the \textit{Hamming distance}.
+The Hamming distance between two codewords is defined as the number of bit positions in which they differ.
+For example, the codewords $10110$ and $11100$ have a Hamming distance of 2.
+
+A code with a minimum Hamming distance $d_{min}$ can detect up to $d_{min}-1$ errors
+and correct up to $\lfloor (d_{min}-1)/2 \rfloor$ errors.
+This insight forms the basis of error-correcting codes such as Hamming codes,
+which add redundant bits to data in a structured way that enables the receiver to both identify and correct single-bit errors.
+
+Thus, the efficiency and reliability of communication systems are governed by a trade-off:
+higher redundancy (lower efficiency) provides greater error correction capability,
+while minimal redundancy maximizes data throughput but reduces error resilience.
+
 %Coding of a source of an information and communication channel
 % https://www.youtube.com/watch?v=ErfnhcEV1O8
 % relation to hamming distance and efficient codes
@@ -218,25 +252,11 @@ true label vector is assumed to be the unit vector $e_i$ (one-hot).
 \subsection{Noisy communication channels}
 The noisy channel coding theorem was stated by \textit{Claude Shannon} in 1948, but first rigorous proof was
 provided in 1954 by Amiel Feinstein.
-One of the important issues Shannon wanted to tackle with his 'Mathematical theory of commmunication'
+One of the important issues Shannon tackled with his 'Mathematical theory of commmunication'
 was the insufficient means of transporting discrete data through a noisy channel that were more efficient than
-the telegram.
+the telegram - or, how to communicate reliably over an unreliable channel.
 The means of error correction until then had been limited to very basic means.
 
-First, analogue connections like the first telephone lines, bypassed the issue altogether and relied
-on the communicating parties and their brains' ability to filter human voices from the noise that was inevitably transmitted
-along with the intended signal.
-After some development, the telegraph in its final form used morse code, a series of long and short clicks, that,
-together with letter and word gaps, would encode text messages.
-Even though the long-short coding might appear similar to todays binary coding, the means of error correction were lacking.
-For a long time, it relied on simply repeating the message multiple times, which is highly inefficient.
-The destination would then have to determine the most likely intended message by performing a majority vote.
-One might also propose simply increasing transmitting power, thereby decreasing the error rate of the associated channel.
-However, the noisy channel coding theorem provides us with a more elegant solution.
-It is of foundational importance to information theory, stating that given a noisy channel with capacity $C$
-and information transmitted at rate $R$, there exists an $R<C$ so the error rate at the receiver can be
-arbitrarily small.
-
 \begin{figure}[H]
 \begin{tikzpicture}
     \def\boxw{2.5cm}
@@ -260,6 +280,39 @@ arbitrarily small.
 \caption{Model of a noisy communication channel}
 \label{fig:noisy-channel}
 \end{figure}
+First, analogue connections like the first telephone lines, bypassed the issue altogether and relied
+on the communicating parties and their brains' ability to filter human voices from the noise that was inevitably transmitted
+along with the intended signal.
+After some development, the telegraph in its final form used morse code, a series of long and short clicks, that,
+together with letter and word gaps, would encode text messages.
+Even though the long-short coding might appear similar to todays binary coding, the means of error correction were lacking.
+For a long time, it relied on simply repeating the message multiple times, which is highly inefficient.
+The destination would then have to determine the most likely intended message by performing a majority vote.
+One might also propose simply increasing transmitting power, thereby decreasing the error rate of the associated channel.
+However, the noisy channel coding theorem provides us with a more elegant solution.
+It is of foundational importance to information theory, stating that given a noisy channel with capacity $C$
+and information transmitted at rate $R$, there exists an $R<C$ so the error rate at the receiver can be
+arbitrarily small.
+
+
+
+\paragraph{Channel capacity and mutual information}
+For any discrete memoryless channel, we can describe its behavior with a conditional probability distribution
+$p(y|x)$ — the probability that symbol $y$ is received given symbol $x$ was sent.
+The \textit{mutual information} between the transmitted and received signals measures how much information, on average, passes through the channel:
+\begin{equation}
+    I(X;Y) = \sum_{x,y} p(x, y) \log_2 \frac{p(x, y)}{p(x)p(y)} = H(Y) - H(Y|X)
+\end{equation}
+The \textit{channel capacity} $C$ is then defined as the maximum achievable mutual information across all possible input distributions:
+\begin{equation}
+    C = \max_{p(x)} I(X;Y)
+\end{equation}
+It represents the highest rate (in bits per symbol) at which information can be transmitted with arbitrarily small error,
+given optimal encoding and decoding schemes.
+
+\paragraph{Binary symmetric channel (BSC)}
+The binary symmetric channel is one example of such discrete memoryless channels, where each transmitted bit
+has a probability $p$ of being flipped during transmission and a probability $(1-p)$ of being received correctly.
 
 \begin{figure}[H]
 \begin{tikzpicture}
@@ -284,10 +337,24 @@ arbitrarily small.
     \draw[->] (D0) -- (D);
     \draw[->] (D1) -- (D);
 \end{tikzpicture}
-\caption{Model of a binary symmetric channel}
+\caption{Binary symmetric channel with crossover probability $p$}
 \label{fig:binary-channel}
 \end{figure}
 
+The capacity of the binary symmetric channel is given by:
+\begin{equation}
+    C = 1 - H_2(p)
+\end{equation}
+where $H_2(p) = -p \log_2(p) - (1-p)\log_2(1-p)$ is the binary entropy function.
+As $p$ increases, uncertainty grows and channel capacity declines.
+When $p = 0.5$, output bits are completely random and no information can be transmitted ($C = 0$).
+As already shown in \autoref{fig:graph-entropy}, an error rate over $p > 0.5$ is equivalent to $ 1-p < 0.5$,
+though not relevant in practice.
+
+Shannon’s theorem is not constructive as it does not provide an explicit method for constructing such efficient codes,
+but it guarantees their existence.
+In practice, structured codes such as Hamming and Reed–Solomon codes are employed to approach channel capacity.
+
 \printbibliography
 
 \end{document}
\ No newline at end of file