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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Beyond Bayesian Model Averaging over Paths in Probabilistic Programs with Stochastic Support · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2310.14888/</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/2310.14888/feed.xml" rel="self" type="application/rss+xml" /><item><title>Beyond Bayesian Model Averaging over Paths in Probabilistic Programs with Stochastic Support — mentioned by btown</title><link>https://news.ycombinator.com/item?id=43163417</link><guid isPermaLink="false">urn:hackerpapers:mention:2310.14888:43163417</guid><pubDate>Mon, 24 Feb 2025 18:59:57 GMT</pubDate><description>&lt;p&gt;And going beyond this: my layman&amp;#x27;s understanding of biology is that the way in which genes are expressed can be highly nonlinear and modulated by all sorts of different pathways. If you have some clarity on how these pathways work, probabilistic programming might be a helpful tool here in a Bayesian context. It&amp;#x27;s been a number of years since I&amp;#x27;ve looked at these things, but https://www.theactuary.com/2024/04/04/bayesian-revolution and https://arxiv.org/abs/2310.14888 are recent articles that may be relevant.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2310.14888/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item></channel></rss>
