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		<title>Estimating PDFs with sparse data</title>
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			<title>Reply #1</title>
			<link>https://www.lotterypost.com/thread/345335/7258003</link>
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			<pubDate>Wed, 10 May 2023 21:13:49 GMT</pubDate>
			<dc:creator>eddessaknight</dc:creator>
			<description><![CDATA[<p>Good Post Wavepack</p>]]></description>
			<category>eddessaknight</category>
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			<title>Estimating PDFs with sparse data</title>
			<link>https://www.lotterypost.com/thread/345335</link>
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			<pubDate>Wed, 10 May 2023 02:24:06 GMT</pubDate>
			<dc:creator>Wavepack</dc:creator>
			<description><![CDATA[<p>Estimate a multinomial pdf, p t = [p 1 , ... , p m ], of a random variable with m states using N samples of the random variable, given that the prior pdf estimate, p t-1 is a uniform pdf. Denote the sample count as n = [n 1 , ... , n m ].<br /><br />Why? Because taking a pure frequentest estimation of p n / N, which doesn&#x27;t take into account prior pdf information, and results in an estimate with too many zero entries, because of sparse data. That&#x27;s the case when dealing with 2nd order statistics and li... &#x5b;&#xa0;<a href="https://www.lotterypost.com/thread/345335">More</a>&#xa0;&#x5d;</p>]]></description>
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