<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Simulation on gdpark.blog</title><link>https://gdpark.blog/tags/simulation/</link><description>Recent content in Simulation on gdpark.blog</description><generator>Hugo</generator><language>en</language><lastBuildDate>Thu, 26 Sep 2019 00:00:00 +0000</lastBuildDate><atom:link href="https://gdpark.blog/tags/simulation/index.xml" rel="self" type="application/rss+xml"/><item><title>Monte Carlo Method [Financial Engineering Programming #9]</title><link>https://gdpark.blog/posts/financial-engineering-09-monte-carlo-method/</link><pubDate>Thu, 20 Oct 2016 00:00:00 +0000</pubDate><guid>https://gdpark.blog/posts/financial-engineering-09-monte-carlo-method/</guid><description>Stumbled into a stats lecture and finally got it — turns out if you scatter enough random dots and do a little ratio math, you can actually calculate π. Wild, right?</description></item><item><title>Estimating Pi Using the Monte Carlo Method in Python [Financial Engineering Programming #23]</title><link>https://gdpark.blog/posts/financial-engineering-23-estimating-pi-using-the-monte-carlo-method-in-python/</link><pubDate>Thu, 26 Sep 2019 00:00:00 +0000</pubDate><guid>https://gdpark.blog/posts/financial-engineering-23-estimating-pi-using-the-monte-carlo-method-in-python/</guid><description>Toss enough random darts at a unit square, count the ones inside the circle, and — boom — you&amp;rsquo;ve got π, courtesy of the Monte Carlo method and a bit of Python.</description></item></channel></rss>