---
title: "SU-ENGR76 APR042024"
source: https://www.jemoka.com/posts/kbhsu_engr76_apr042024/
date: 2024-04-04
---

information Information is the amount of surprise a message provides.
Shannon (1948): a mathematical theory for communication.
information value The information value, or entropy, of a information source is its probability-weighted average surprise of all possible outcomes:
\begin{equation} H(X) = \sum_{x \in X}^{} s(P(X=x)) P(X=x) \end{equation}
properties of entropy entropy is positive: \(H(x) \geq 0\) entropy of uniform: for \(M \sim CatUni[1, &hellip;, n]\), \(p_{i} = \frac{1}{|M|} = \frac{1}{n}\), and \(H(M) = \log_{2} |M| = \log_{2} n\) entropy is bounded: \(0 \leq H(X) \leq H(M)\) where \(|X| = |M|\) and \(M \sim CatUni[1 &hellip; n]\) (&ldquo;uniform distribution has the highest entropy&rdquo;); we will reach the upper bound IFF \(X\) is uniformly distributed. binary entropy function For some binary outcome \(X \in \{1,2\}\), where \(P(x=1) = p_1\), \(P(X_2 = 2) = 1-p_1\). We can write:
\begin{equation} H_{2}(p_1) = p_1 \log_{2} \frac{1}{p_1} + (1-p_1) \log_{2} \frac{1}{1-p_1} \end{equation}
If you plot this out, we get a cap-like function, whereby at \(H(0) = 0\), \(H(1) = 0\), but \(H(0.5) = 1\) &mdash; information sources are most effective when what&rsquo;s communicated is ambiguous.
information source We model an information source as an random variable. A random variable can take on any number of information, until you GET the information, and you will get an exact value. Each source has a range of possible values it can communicate (which is the support of the random variable representing the information source).
We will then define the surprise of a piece of information as a (decreasing function? of) the probability corresponding to the event of receiving that information.
surprise IMPORTANTLY: this class uses base \(2\), but the base is unimportant.
\begin{equation} s(p) = \log_{2} \frac{1}{p} \end{equation}
Properties of Surprise log-base-2 surprise has units &ldquo;bits&rdquo; \(s(1) = 0\) \(p \to 0, s(p) \to \infty\) \(s(p) \geq 0\) &ldquo;joint surprise&rdquo; \(s(p,q) = s(p) + s(q)\) Facts about Surprise surprise should probably decrease with increasing \(p\) surprise should be continuous in \(p\) for \(s(p_i), s(q_{j})\), for two events \(p_i\) and \(q_{j}\), the &ldquo;surprise&rdquo; of \(s(pi, q_{j})\) = \(s(p_{i}) + s(q_{j})\) surprise satisfies the fact that something with probably \(0\) happening, we should be infinitely surprises; if something happens with iincreasing higher probabiity, surprise would be low The surprise function is the unique function which satisfies all of the above property.
additional information information is relative to the domain which is attempted to be communicated
