<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Convergence on Chen Kai Blog</title><link>https://www.chenk.top/en/tags/convergence/</link><description>Recent content in Convergence on Chen Kai Blog</description><generator>Hugo</generator><language>en</language><lastBuildDate>Sat, 24 Aug 2024 09:00:00 +0000</lastBuildDate><atom:link href="https://www.chenk.top/en/tags/convergence/index.xml" rel="self" type="application/rss+xml"/><item><title>Probability and Statistics (5): Law of Large Numbers and the Central Limit Theorem</title><link>https://www.chenk.top/en/probability-statistics/05-limit-theorems/</link><pubDate>Sat, 24 Aug 2024 09:00:00 +0000</pubDate><guid>https://www.chenk.top/en/probability-statistics/05-limit-theorems/</guid><description>&lt;p>If you had to choose just two theorems from all of probability theory, you&amp;rsquo;d choose these: the Law of Large Numbers (LLN) and the Central Limit Theorem (CLT). Together, they answer two fundamental questions. The LLN says: &amp;ldquo;Yes, your sample average will converge to the true mean.&amp;rdquo; The CLT says: &amp;ldquo;And here&amp;rsquo;s exactly what the fluctuations look like.&amp;rdquo; Without these theorems, there&amp;rsquo;s no justification for opinion polls, no reason to trust clinical trials, and no explanation for why stochastic gradient descent converges.&lt;/p></description></item></channel></rss>