<?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>概率论 on s-ai-unix's Blog</title><link>https://s-ai-unix.github.io/tags/%E6%A6%82%E7%8E%87%E8%AE%BA/</link><description>Recent content in 概率论 on s-ai-unix's Blog</description><generator>Hugo -- 0.161.1</generator><language>zh-cn</language><lastBuildDate>Tue, 03 Feb 2026 08:30:00 +0800</lastBuildDate><atom:link href="https://s-ai-unix.github.io/tags/%E6%A6%82%E7%8E%87%E8%AE%BA/index.xml" rel="self" type="application/rss+xml"/><item><title>数理统计重要定理系列：大数定律与中心极限定理的深度解读</title><link>https://s-ai-unix.github.io/posts/2026-02-03-statistical-foundations-law-of-large-numbers-and-central-limit-theorem/</link><pubDate>Tue, 03 Feb 2026 08:30:00 +0800</pubDate><guid>https://s-ai-unix.github.io/posts/2026-02-03-statistical-foundations-law-of-large-numbers-and-central-limit-theorem/</guid><description>系统梳理大数定律和中心极限定理的历史背景、数学推导和实际应用，揭示这两个统计基石如何在随机性与确定性之间架起桥梁</description></item><item><title>概率论与数理统计：机器学习的概率基石</title><link>https://s-ai-unix.github.io/posts/2026-01-25-probability-statistics-ml-guide/</link><pubDate>Sun, 25 Jan 2026 12:00:00 +0800</pubDate><guid>https://s-ai-unix.github.io/posts/2026-01-25-probability-statistics-ml-guide/</guid><description>从赌场轮盘到神经网络：系统性地介绍概率论和数理统计在机器学习中的核心应用，包含完整的数学推导和直观的几何可视化</description></item><item><title>蒙特卡罗算法：从原子弹到人工智能的随机之旅</title><link>https://s-ai-unix.github.io/posts/2026-01-21-monte-carlo-method/</link><pubDate>Wed, 21 Jan 2026 23:00:00 +0800</pubDate><guid>https://s-ai-unix.github.io/posts/2026-01-21-monte-carlo-method/</guid><description>从曼哈顿计划的保密代号到现代科学计算的核心工具，本文系统介绍蒙特卡罗方法的发展历程、数学基础和广泛应用。</description></item><item><title>正态分布：从赌桌到宇宙的完美曲线</title><link>https://s-ai-unix.github.io/posts/2026-01-21-gaussian-distribution-history/</link><pubDate>Wed, 21 Jan 2026 10:00:00 +0800</pubDate><guid>https://s-ai-unix.github.io/posts/2026-01-21-gaussian-distribution-history/</guid><description>正态分布是概率论中最重要、最自然的分布。本文将娓娓道来，讲述这条曲线如何从17世纪的赌桌、天文观测中逐渐浮现，最终成为描述随机现象的普适语言。</description></item><item><title>贝叶斯公式：从牧师遗作到人工智能基石</title><link>https://s-ai-unix.github.io/posts/2026-01-21-bayes-theorem/</link><pubDate>Wed, 21 Jan 2026 10:00:00 +0800</pubDate><guid>https://s-ai-unix.github.io/posts/2026-01-21-bayes-theorem/</guid><description>本文系统梳理贝叶斯公式从18世纪诞生到21世纪成为人工智能核心方法的完整发展历程，包含严谨的数学推导、丰富的历史背景和现代应用案例。</description></item></channel></rss>