<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Joint Distributions on Chen Kai Blog</title><link>https://www.chenk.top/en/tags/joint-distributions/</link><description>Recent content in Joint Distributions on Chen Kai Blog</description><generator>Hugo</generator><language>en</language><lastBuildDate>Fri, 23 Aug 2024 09:00:00 +0000</lastBuildDate><atom:link href="https://www.chenk.top/en/tags/joint-distributions/index.xml" rel="self" type="application/rss+xml"/><item><title>Probability and Statistics (4): Joint Distributions, Marginalization, and Independence</title><link>https://www.chenk.top/en/probability-statistics/04-joint-distributions/</link><pubDate>Fri, 23 Aug 2024 09:00:00 +0000</pubDate><guid>https://www.chenk.top/en/probability-statistics/04-joint-distributions/</guid><description>&lt;p>Until now, every distribution we&amp;rsquo;ve studied described a single quantity: one die roll, one waiting time, one measurement. But interesting problems involve relationships between variables. Does studying more hours predict a higher exam score? Are stock returns correlated across sectors? How does the sum of two random variables behave?&lt;/p>
&lt;p>Answering these questions requires &lt;strong>joint distributions&lt;/strong> — the mathematical framework for describing multiple random variables simultaneously. This is where probability theory starts connecting directly to regression, multivariate statistics, and the high-dimensional spaces of machine learning.&lt;/p></description></item></channel></rss>