<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Parameter-Estimation on Aayush Bajaj's Augmenting Infrastructure</title><link>https://abaj.ai/tags/parameter-estimation/</link><description>Recent content in Parameter-Estimation 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/parameter-estimation/index.xml" rel="self" type="application/rss+xml"/><item><title>Learning Graphical Models</title><link>https://abaj.ai/wiki/ml/pgm/learning/</link><pubDate>Mon, 31 Aug 2026 00:29:54 +1000</pubDate><guid>https://abaj.ai/wiki/ml/pgm/learning/</guid><description>&lt;p>everything so far assumed the network was handed to us. in practice an expert sketches the graph at best, and the numbers — sometimes the graph too — must come from data. learning splits along two axes: &lt;em>parameters vs structure&lt;/em>, and &lt;em>complete vs incomplete data&lt;/em>. the four quadrants ascend steeply in difficulty: counting, then optimisation, then search over graphs, then all three at once.&lt;span class="margin-note" data-note="this is the pipeline of the comp9418 assignment: learn the outcome spaces and cpts from an icu dataset, then classify with complete and with missing evidence">
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