X-Git-Url: https://www.fleuret.org/cgi-bin/gitweb/gitweb.cgi?p=tex.git;a=blobdiff_plain;f=inftheory.tex;fp=inftheory.tex;h=954fd0630c48e3636e6923a34afeaf44344f87b4;hp=7c34fce40c8b14b95d07e29c86c16aa3ffd21d73;hb=74cdd5e14b65ac1ff03725173eb941dc7a455edf;hpb=cf0fd332cb70bf1a2a793ce658770da5ca702db9

diff --git a/inftheory.tex b/inftheory.tex
index 7c34fce..954fd06 100644
--- a/inftheory.tex
+++ b/inftheory.tex
@@ -4,8 +4,8 @@
 %% https://creativecommons.org/publicdomain/zero/1.0/
 %% Written by Francois Fleuret <francois@fleuret.org>
 
-\documentclass[10pt,a4paper,twoside]{article}
-\usepackage[paperheight=18cm,paperwidth=10cm,top=5mm,bottom=20mm,right=5mm,left=5mm]{geometry}
+\documentclass[11pt,a4paper,oneside]{article}
+\usepackage[paperheight=15cm,paperwidth=8cm,top=2mm,bottom=15mm,right=2mm,left=2mm]{geometry}
 %\usepackage[a4paper,top=2.5cm,bottom=2cm,left=2.5cm,right=2.5cm]{geometry}
 \usepackage[utf8]{inputenc}
 \usepackage{amsmath,amssymb,dsfont}
@@ -20,6 +20,8 @@
 \usetikzlibrary{tikzmark}
 \usetikzlibrary{decorations.pathmorphing}
 \usepackage[round]{natbib}
+\usepackage[osf]{libertine}
+\usepackage{microtype}
 
 \usepackage{mleftright}
 
@@ -29,7 +31,7 @@
 \setmuskip{\thickmuskip}{3.5mu} % by default it is equal to 5 mu
 
 \setlength{\parindent}{0cm}
-\setlength{\parskip}{12pt}
+\setlength{\parskip}{1ex}
 %\renewcommand{\baselinestretch}{1.3}
 %\setlength{\tabcolsep}{0pt}
 %\renewcommand{\arraystretch}{1.0}
@@ -65,12 +67,17 @@
 
 \begin{document}
 
-\vspace*{1ex}
+\vspace*{-3ex}
 
 \begin{center}
 {\Large Some bits of Information Theory}
 
-\today
+Fran\c cois Fleuret
+
+January 19, 2024
+
+\vspace*{1ex}
+
 \end{center}
 
 Information Theory is awesome so here is a TL;DR about Shannon's entropy.
@@ -79,9 +86,9 @@ The field is originally about quantifying the amount of
 ``information'' contained in a signal and how much can be transmitted
 under certain conditions.
 
-What makes it awesome IMO is that it is very intuitive, and like
-thermodynamics in Physics, it gives exact bounds about what is possible
-or not.
+What makes it awesome is that it is very intuitive, and like
+thermodynamics in Physics, it gives exact bounds about what is
+possible or not.
 
 \section{Shannon's Entropy}
 
@@ -96,8 +103,8 @@ line, you can design a coding that takes into account that the symbols
 are not all as probable, and decode on the other side.
 
 For instance if $\proba('\!\!A')=1/2$, $\proba('\!\!B')=1/4$, and
-$\proba('\!\!C')=1/4$ you would transmit ``0'' for a ``A'' and ``10'' for a
-``B'' and ``11'' for a ``C'', 1.5 bits on average.
+$\proba('\!\!C')=1/4$ you would transmit ``$0$'' for a ``A'' and ``$10$'' for a
+``B'' and ``$11$'' for a ``C'', 1.5 bits on average.
 
 If the symbol is always the same, you transmit nothing, if they are
 equiprobable you need $\log_2$(nb symbols) etc.
@@ -153,11 +160,16 @@ that quantifies the amount of information shared by the two variables.
 
 Conditional entropy is the average of the entropy of the conditional distribution:
 %
-\begin{align*}
- & \entropy(X \mid Y)                        \\
- & = \sum_y \proba(Y=y) \entropy(X \mid Y=y) \\
- & = \sum_y \proba(Y=y) \sum_x \proba(X=x \mid Y=y) \log \proba(X=x \mid Y=y)
-\end{align*}
+\begin{equation*}
+\entropy(X \mid Y) = \sum_y \proba(Y=y) \entropy(X \mid Y=y)
+\end{equation*}
+%
+with
+%
+\begin{eqnarray*}
+\entropy(X \mid Y=y) \hspace*{13.5em} \\
+ = \sum_x \proba(X=x \mid Y=y) \log \proba(X=x \mid Y=y)
+\end{eqnarray*}
 
 Intuitively it is the [minimum average] number of bits required to describe $X$ given that $Y$ is known.
 
@@ -175,13 +187,13 @@ and if $X$ is a deterministic function of $Y$ then
   \entropy(X \mid Y)=0.
 \]
 
-And if you send the bits for $Y$ and then the bits to describe $X$ given
-that $Y$, you have sent $(X, Y)$. Hence we have the chain rule:
+And if you send the bits for $Y$ and then the bits to describe $X$
+given that $Y$, you have sent $(X, Y)$, hence the chain rule:
 %
 \[
 \entropy(X, Y) = \entropy(Y) + \entropy(X \mid Y).
 \]
-
+%
 And then we get
 %
 \begin{align*}