<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Jointree on Aayush Bajaj's Augmenting Infrastructure</title><link>https://abaj.ai/tags/jointree/</link><description>Recent content in Jointree on Aayush Bajaj's Augmenting Infrastructure</description><generator>Hugo</generator><language>en</language><copyright>© 2026 Aayush Bajaj</copyright><lastBuildDate>Mon, 31 Aug 2026 00:29:54 +1000</lastBuildDate><atom:link href="https://abaj.ai/tags/jointree/index.xml" rel="self" type="application/rss+xml"/><item><title>Exact Inference</title><link>https://abaj.ai/wiki/ml/pgm/inference/</link><pubDate>Mon, 31 Aug 2026 00:29:54 +1000</pubDate><guid>https://abaj.ai/wiki/ml/pgm/inference/</guid><description>&lt;p>a &lt;a
 href="https://abaj.ai/wiki/ml/pgm/bayesian-networks/"
 
 
>bayesian network&lt;/a> stores a joint distribution; inference is spending that investment — computing probabilities the tables do not state directly. exact inference is np-hard in general, but its true cost is exponential only in a structural parameter of the graph (the &lt;em>treewidth&lt;/em>), not in the number of variables. this page builds the exact toolchain in the order the ideas force themselves on you: factors, variable elimination, the fight for a good elimination order, and finally the jointree algorithm — variable elimination industrialised into a message-passing scheme that answers every marginal at once.&lt;span class="margin-note" data-note="when the treewidth is too large, exact methods drown and you switch to the sampling algorithms on the approximate inference page">
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