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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Combining Graphical and Algebraic Approaches for Parameter Identification in Latent Variable Structural Equation Models · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2302.13220/</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/2302.13220/feed.xml" rel="self" type="application/rss+xml" /><item><title>Combining Graphical and Algebraic Approaches for Parameter Identification in Latent Variable Structural Equation Models — mentioned by null08</title><link>https://news.ycombinator.com/item?id=41454954</link><guid isPermaLink="false">urn:hackerpapers:mention:2302.13220:41454954</guid><pubDate>Thu, 05 Sep 2024 09:20:55 GMT</pubDate><description>&lt;p&gt;The connection between SEMs and DAGs is really interesting. The underlying models are very similar but developed independently -- SEMs coming from the psychometrics tradition and popularized by Jöreskog, DAGs coming from Bayesian Networks and popularized by Pearl. There are deep connections between them -- we have done some work on that (e.g. https://osf.io/preprints/psyarxiv/2kqxr and https://arxiv.org/abs/2302.13220).&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2302.13220/"&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>
