<?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>Bernoulli on Karpoke - Just Another Blog</title><link>http://karpoke.ignaciocano.com/tags/bernoulli/</link><description>Recent content in Bernoulli on Karpoke - Just Another Blog</description><generator>Hugo -- 0.159.0</generator><language>es</language><lastBuildDate>Mon, 09 Jul 2012 20:07:00 +0100</lastBuildDate><atom:link href="http://karpoke.ignaciocano.com/tags/bernoulli/index.xml" rel="self" type="application/rss+xml"/><item><title>Pitfalls in Random Number Generation</title><link>http://karpoke.ignaciocano.com/2012/07/09/pitfalls-in-random-number-generation/</link><pubDate>Mon, 09 Jul 2012 20:07:00 +0100</pubDate><guid>http://karpoke.ignaciocano.com/2012/07/09/pitfalls-in-random-number-generation/</guid><description>&lt;blockquote&gt;
&lt;p&gt;Random number generation is subtle. Random number generators contain
deterministic algorithms designed to produce output that simulates
non-deterministic behavior. It’s amazing that there are algorithms that do
this well enough for many applications. But unless used carefully, random
number generators can misbehave in mysterious ways.&lt;/p&gt;
&lt;/blockquote&gt;
&lt;p&gt;» John D. Cook | &lt;a href="http://www.codeproject.com/Articles/28548/Pitfalls-in-Random-Number-Generation"&gt;codeproject.com&lt;/a&gt;&lt;/p&gt;</description></item></channel></rss>