Though originally developed for communications engineering, information theory provides mathematical tools with broad applications across science and engineering. These tools characterize the fundamental limits of data compression and transmission in the presence of noise. Here, we present a visual, intuition-driven guide to key concepts in information theory, showing how entropy, mutual information, and channel capacity arise from probability and govern these limits. Our presentation assumes only a familiarity with basic probability theory.

翻译：尽管最初为通信工程而发展，信息论提供了在科学与工程领域具有广泛应用的数学工具。这些工具刻画了在噪声存在下数据压缩与传输的基本极限。本文通过视觉化、直觉驱动的方式介绍信息论的核心概念，展示熵、互信息与信道容量如何从概率论中产生并支配这些极限。我们的阐述仅需读者具备基础概率论知识。