Advanced & Applied Topics · Information Theory & Entropy

Information theory · modern applications — statistical and computational foundations

Public introduction · full guide in Eikos

Introduction

What this guide covers

Introduction Information Analysis and Computing

Overview

Topics covered

  • Introduction
  • Information Theory and its modern applications
  • Fundamentals and Shannon Entropy
  • 1.1 Khinchin's axiomatic derivation
  • 1.2 Joint and Conditional Entropy
  • 1.3 Mutual Information

Related concepts

Main connections

  • Calculus & differentiation
  • Linear algebra & matrices
  • Maximum likelihood estimation
  • Mean & expected value
  • Normal (Gaussian) distribution
  • Mathematical expectation & moments

Compatible notation

Mathematical sample in LaTeX

The expression is rendered accessibly, with its LaTeX source preserved below.

I(xi)=−logbpi=logb(1pi)
View LaTeX sourceI(x_i) = -\log_b p_i = \log_b \left(\frac{1}{p_i}\right)