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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Dimension-Free Empirical Entropy Estimation · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2105.07408/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/2105.07408/feed.xml" rel="self" type="application/rss+xml" /><item><title>Dimension-Free Empirical Entropy Estimation — mentioned by cb321</title><link>https://news.ycombinator.com/item?id=40393805</link><guid isPermaLink="false">urn:hackerpapers:mention:2105.07408:40393805</guid><pubDate>Fri, 17 May 2024 20:18:32 GMT</pubDate><description>&lt;p&gt;Besides the ideas from istjohn, empath-nirvana, and rcarmo, you can also just &amp;quot;flip the script&amp;quot;: solve for epsilon and report that as 1-delta confidence interval for the worst case data distribution as here: https://news.ycombinator.com/item?id=40388878 Best case error is of course zero, but if you look at my output then you will see as I did that the worst case is a very conservative bound (i.e. 15X bigger than what might &amp;quot;tend to happen&amp;quot;. That matters a lot for &amp;quot;space usage&amp;quot; since the error =~ 1/sqrt(space) implying you need a lot more space for lower errors. 15^2 = 225X more space. Space…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2105.07408/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.IT</category></item></channel></rss>
