<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Expectation on Chen Kai Blog</title><link>https://www.chenk.top/en/tags/expectation/</link><description>Recent content in Expectation on Chen Kai Blog</description><generator>Hugo</generator><language>en</language><lastBuildDate>Wed, 21 Aug 2024 09:00:00 +0000</lastBuildDate><atom:link href="https://www.chenk.top/en/tags/expectation/index.xml" rel="self" type="application/rss+xml"/><item><title>Probability and Statistics (3): Expectation, Variance, and the Moment-Generating Trick</title><link>https://www.chenk.top/en/probability-statistics/03-expectation-and-moments/</link><pubDate>Wed, 21 Aug 2024 09:00:00 +0000</pubDate><guid>https://www.chenk.top/en/probability-statistics/03-expectation-and-moments/</guid><description>&lt;p>A probability distribution is a complete description of a random variable — it tells you the probability of every possible outcome. But complete descriptions are unwieldy. When someone asks &amp;ldquo;how tall are people in this city?&amp;rdquo;, you don&amp;rsquo;t hand them a density function; you say &amp;ldquo;about 170 cm on average, give or take 10 cm.&amp;rdquo; The average and the spread capture most of what matters in practice.&lt;/p>
&lt;p>This article develops the mathematical framework for summarizing distributions. We start with expectation (the center), build up to variance (the spread), and then introduce moment-generating functions — a single formula that encodes every moment of a distribution and, remarkably, uniquely determines the distribution itself.&lt;/p></description></item></channel></rss>