<?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>Statistics on Yeongseung's Blog</title><link>https://yeongseung.github.io/tags/statistics/</link><description>Recent content in Statistics on Yeongseung's Blog</description><generator>Hugo -- 0.160.1</generator><language>en</language><lastBuildDate>Thu, 05 Mar 2026 17:10:26 +0900</lastBuildDate><atom:link href="https://yeongseung.github.io/tags/statistics/index.xml" rel="self" type="application/rss+xml"/><item><title>Statistical Notes: Questions, Clarifications, and Intuition</title><link>https://yeongseung.github.io/posts/statistics/post/</link><pubDate>Thu, 05 Mar 2026 17:10:26 +0900</pubDate><guid>https://yeongseung.github.io/posts/statistics/post/</guid><description>&lt;h1 id="introduction"&gt;Introduction&lt;/h1&gt;
&lt;p&gt;This blog reflects my journey through Machine Learning, Reinforcement Learning, and Robotics. While my current work spans these areas, my academic roots are in Statistics, a foundation that continues to shape how I think about problems. From a theoretical standpoint, I believe Statistics remains the core foundation of modern machine learning.&lt;/p&gt;
&lt;p&gt;Over time, while studying statistics, I often encountered concepts that felt confusing, subtle, or easy to misunderstand. Some of them became clearer after revisiting the ideas multiple times or thinking about them from different perspectives, while others are still things I occasionally find myself reflecting on.&lt;/p&gt;</description></item></channel></rss>