<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Random Variables on Chen Kai Blog</title><link>https://www.chenk.top/en/tags/random-variables/</link><description>Recent content in Random Variables on Chen Kai Blog</description><generator>Hugo</generator><language>en</language><lastBuildDate>Tue, 20 Aug 2024 09:00:00 +0000</lastBuildDate><atom:link href="https://www.chenk.top/en/tags/random-variables/index.xml" rel="self" type="application/rss+xml"/><item><title>Probability and Statistics (2): Random Variables and the Distributions That Matter</title><link>https://www.chenk.top/en/probability-statistics/02-random-variables/</link><pubDate>Tue, 20 Aug 2024 09:00:00 +0000</pubDate><guid>https://www.chenk.top/en/probability-statistics/02-random-variables/</guid><description>&lt;p>After building the axiomatic foundation in the previous article, you might feel like we spent a lot of time talking about sets and subsets. That&amp;rsquo;s because we did. The machinery of events and sigma-algebras is necessary but austere — it doesn&amp;rsquo;t give us a natural way to compute averages, measure spread, or fit models to data.&lt;/p>
&lt;p>The bridge between abstract probability and applied statistics is the &lt;strong>random variable&lt;/strong>. Once we assign numerical values to outcomes, the entire toolkit of calculus — derivatives, integrals, series — becomes available. And with calculus comes the ability to characterize randomness through a small set of named distributions, each encoding specific assumptions about how the world generates data.&lt;/p></description></item></channel></rss>