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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Learning Approximately Objective Priors · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1704.01168/</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/1704.01168/feed.xml" rel="self" type="application/rss+xml" /><item><title>Learning Approximately Objective Priors — mentioned by ta1929901</title><link>https://news.ycombinator.com/item?id=15103176</link><guid isPermaLink="false">urn:hackerpapers:mention:1704.01168:15103176</guid><pubDate>Fri, 25 Aug 2017 23:48:08 GMT</pubDate><description>&lt;p&gt;A lot of what they discuss is in the literature on reference priors, if not in other literature on objective priors as well. It&amp;#x27;s a little complex for a comment on HN, but IMHO the best formalization of overfitting is in the literature on minimum description length, and related information-theoretic literature (https://en.wikipedia.org/wiki/Minimum_description_length ; the wikipedia page is a little off on some things but the general points are probably about right). The relationship between MDL/NML and Bayesian statistics is a little complicated--Barron, Roos, and Watanabe have a nice recent…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1704.01168/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>stat.ML</category></item></channel></rss>
