<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>信息论 on s-ai-unix's Blog</title><link>https://s-ai-unix.github.io/tags/%E4%BF%A1%E6%81%AF%E8%AE%BA/</link><description>Recent content in 信息论 on s-ai-unix's Blog</description><generator>Hugo -- 0.161.1</generator><language>zh-cn</language><lastBuildDate>Tue, 03 Feb 2026 08:36:00 +0800</lastBuildDate><atom:link href="https://s-ai-unix.github.io/tags/%E4%BF%A1%E6%81%AF%E8%AE%BA/index.xml" rel="self" type="application/rss+xml"/><item><title>数理统计重要定理系列：最大熵原理与高斯分布的自然选择</title><link>https://s-ai-unix.github.io/posts/2026-02-03-maximum-entropy-principle-gaussian-distribution/</link><pubDate>Tue, 03 Feb 2026 08:36:00 +0800</pubDate><guid>https://s-ai-unix.github.io/posts/2026-02-03-maximum-entropy-principle-gaussian-distribution/</guid><description>深入探讨最大熵原理的数学基础、严格证明及其深刻含义，揭示为什么在已知均值和方差的条件下，高斯分布是自然界最合理的选择</description></item><item><title>数理统计重要定理系列：KL散度的信息论本质与统计应用</title><link>https://s-ai-unix.github.io/posts/2026-02-03-kl-divergence-information-theory/</link><pubDate>Tue, 03 Feb 2026 08:30:00 +0800</pubDate><guid>https://s-ai-unix.github.io/posts/2026-02-03-kl-divergence-information-theory/</guid><description>系统综述KL散度（Kullback-Leibler散度）的历史背景、数学推导、核心性质及其在信息论、统计推断和机器学习中的深刻应用</description></item></channel></rss>