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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>The Fast Loaded Dice Roller: A Near-Optimal Exact Sampler for Discrete Probability Distributions · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2003.03830/</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/2003.03830/feed.xml" rel="self" type="application/rss+xml" /><item><title>The Fast Loaded Dice Roller: A Near-Optimal Exact Sampler for Discrete Probability Distributions — mentioned by binarycoffee</title><link>https://news.ycombinator.com/item?id=23803340</link><guid isPermaLink="false">urn:hackerpapers:mention:2003.03830:23803340</guid><pubDate>Sat, 11 Jul 2020 16:28:58 GMT</pubDate><description>&lt;p&gt;So this is not what the article is about. It discusses a new method to generate integer values according to an arbitrary probability distribution, using as input a uniform random generator. Whether the input generator is truly random, pseudorandom, cryptographically secure etc, is irrelevant: the output will presumably only be as random/secure as the input RNG. Admittedly, the article does a poor job at explaining what the FLDR method is about, and it looks as biased as the method itself (sorry for the easy pun). From my understanding of the paper [1], the method is better than the…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2003.03830/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>stat.CO</category></item><item><title>The Fast Loaded Dice Roller: A Near-Optimal Exact Sampler for Discrete Probability Distributions — mentioned by acqq</title><link>https://news.ycombinator.com/item?id=23802715</link><guid isPermaLink="false">urn:hackerpapers:mention:2003.03830:23802715</guid><pubDate>Sat, 11 Jul 2020 15:08:47 GMT</pubDate><description>&lt;p&gt;The article is not about randomness in general, once one reads the whole. It&amp;#x27;s just the title and the longish intro that can mislead. It&amp;#x27;s about: https://arxiv.org/abs/2003.03830 &amp;quot;The Fast Loaded Dice Roller: A Near-Optimal Exact Sampler for Discrete Probability Distributions&amp;quot; &amp;quot;Empirical evaluations on a broad set of probability distributions establish that FLDR is 2x-10x faster in both preprocessing and sampling than multiple baseline algorithms, including the widely-used alias and interval samplers. It also uses up to 10000x less space than the information-theoretically optimal sampler, at…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2003.03830/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>stat.CO</category></item><item><title>The Fast Loaded Dice Roller: A Near-Optimal Exact Sampler for Discrete Probability Distributions — mentioned by freer</title><link>https://news.ycombinator.com/item?id=23343777</link><guid isPermaLink="false">urn:hackerpapers:mention:2003.03830:23343777</guid><pubDate>Thu, 28 May 2020 22:37:58 GMT</pubDate><description>&lt;p&gt;paper: https://arxiv.org/abs/2003.03830 code: https://github.com/probcomp/fast-loaded-dice-roller&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2003.03830/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>stat.CO</category></item></channel></rss>
