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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Estimating Bernoulli trial probability from a small sample · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1105.1486/</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/1105.1486/feed.xml" rel="self" type="application/rss+xml" /><item><title>Estimating Bernoulli trial probability from a small sample — mentioned by DoctorOetker</title><link>https://news.ycombinator.com/item?id=18468036</link><guid isPermaLink="false">urn:hackerpapers:mention:1105.1486:18468036</guid><pubDate>Fri, 16 Nov 2018 11:31:02 GMT</pubDate><description>&lt;p&gt;I wouldn&amp;#x27;t use a probability density of typo&amp;#x27;s per page, but instead the probability that a word is spelled wrong. Then it&amp;#x27;s like drawing marbles from a vase containing an unknown proportion of blue and red marbles. I would use the formula (M+1)/(N+2) where N is the number of words, and M is the number of mistakes. Note that for a large corpus (M+1)/(N+2) approaches -&amp;gt; M/N, so we recover the frequentist probability. Also note the author (John D Cook) correctly expresses intuitive doubt that 0 typos / 20 pages can not be construed as certainty of no mistakes. Similarily, seeing a mistake in…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1105.1486/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.DC</category></item></channel></rss>
