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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Introduction To Monte Carlo Algorithms · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/cond-mat_9612186/</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/cond-mat_9612186/feed.xml" rel="self" type="application/rss+xml" /><item><title>Introduction To Monte Carlo Algorithms — mentioned by kgwgk</title><link>https://news.ycombinator.com/item?id=15990819</link><guid isPermaLink="false">urn:hackerpapers:mention:cond-mat_9612186:15990819</guid><pubDate>Fri, 22 Dec 2017 20:56:21 GMT</pubDate><description>&lt;p&gt;You can not sample the distribution directly when “it is impossible to solve for analytically”. There are a few methods to overcome this issue, in particular “MCMC methods allow us to estimate the shape of a posterior distribution in case we can’t compute it directly”. You only need to be able to compute the relative values of the function you want to integrate (in this case a probability density) at the current point and the proposed destination. Where is this function coming from is not relevant to understand MCMC methods, they can be applied in many problems unrelated to Bayesian…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/cond-mat_9612186/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cond-mat.stat-mech</category></item></channel></rss>
