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		<title>A shot of the Markov Chain / Follower output</title>
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		<description>hypersoniq's Blog: A shot of the Markov Chain / Follower output</description>
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			<title>Comment #1</title>
			<link>/blogentry/197178#c288000</link>
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			<pubDate>Tue, 10 Feb 2026 11:58:58 GMT</pubDate>
			<dc:creator>hypersoniq</dc:creator>
			<description><![CDATA[<p>Also, instead of a 150 draw sample, this uses the whole history file.</p>]]></description>
			<category>hypersoniq</category>
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			<title>Original Blog Entry: A shot of the Markov Chain / Follower output</title>
			<link>/blogentry/197178</link>
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			<pubDate>Tue, 10 Feb 2026 11:57:55 GMT</pubDate>
			<dc:creator>hypersoniq</dc:creator>
			<description><![CDATA[<p>This is for the pick 2 mid day, the output is simply (number, frequency). The frequency is how many times did these numbers follow the number last drawn. You read from top to bottom and can equate higher frequencies with higher transition probabilities. Keep in mind this is still stale data from the apl as I am working out where to copy the csv files for write access, but the fact that the app launches with zero crashes on my phone still = mind blown... this is pure Python running on Android!<br /><br />Also, this obviously does not actually tell you the winner, as the Markov chain only needs the last state to calculate transition probabilities, but each draw is independent of the last... but it looks good! Had to try.... &#x5b;&#xa0;<a href="/blogentry/197178">More</a>&#xa0;&#x5d;</p>]]></description>
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