<?xml version="1.0" encoding="utf-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:slash="http://purl.org/rss/1.0/modules/slash/" xmlns:wfw="http://wellformedweb.org/CommentAPI/">
	<channel>
		<title>Curious but skeptical</title>
		<link>https://www.lotterypost.com/thread/359885</link>
		<atom:link href="https://www.lotterypost.com/rss/topic/359885" rel="self" type="application/rss+xml" />
		<description>Lottery Post Forum Topic: Curious but skeptical</description>
		<dc:language>en-us</dc:language>
		<generator>Lottery Post RSS Generator</generator>
		<item>
			<title>Curious but skeptical</title>
			<link>https://www.lotterypost.com/thread/359885</link>
			<guid isPermaLink="true">https://www.lotterypost.com/thread/359885</guid>
			<pubDate>Mon, 22 Jun 2026 13:59:04 GMT</pubDate>
			<dc:creator>NumberCruncher2</dc:creator>
			<description><![CDATA[<p>Been lurking a while and finally curious enough to ask. With all the machine-learning stuff everywhere now, I&#x27;ve been tinkering with running models against historical draw data just to see what shakes out Monte Carlo, Markov chains, a couple of neural-net experiments.<br /><br />Upfront: I&#x27;m not claiming any of it beats the odds. Draws are independent and I know how that math works. It&#x27;s more that I find it fun to compare what different methods favor and watch how wildly they disagree with each other o... &#x5b;&#xa0;<a href="https://www.lotterypost.com/thread/359885">More</a>&#xa0;&#x5d;</p>]]></description>
			<category>NumberCruncher2</category>
		</item>
	</channel>
</rss>

