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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Local Gaussian process approximation for large computer experiments · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1303.0383/</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/1303.0383/feed.xml" rel="self" type="application/rss+xml" /><item><title>Local Gaussian process approximation for large computer experiments — mentioned by C-x_C-f</title><link>https://news.ycombinator.com/item?id=44946109</link><guid isPermaLink="false">urn:hackerpapers:mention:1303.0383:44946109</guid><pubDate>Mon, 18 Aug 2025 22:45:31 GMT</pubDate><description>&lt;p&gt;These are very cool, thanks. Do you know what kind of jobs are more likely to require Gaussian process expertise? I have experience in using GP for surrogate modeling and will be on the job market soon. Also a resource I enjoyed is the book by Bobby Gramacy [0] which, among other things, spends a good bit on local GP approximation [1] (and has fun exercises). [0] https://bobby.gramacy.com/surrogates/surrogates.pdf [1] https://arxiv.org/abs/1303.0383&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1303.0383/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>stat.ME</category></item></channel></rss>
