<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Bias-Variance Decomposition on Mira Jürgens</title><link>https://mkjuergens.github.io/tags/bias-variance-decomposition/</link><description>Recent content in Bias-Variance Decomposition on Mira Jürgens</description><generator>Hugo -- 0.147.2</generator><language>en</language><lastBuildDate>Fri, 06 Feb 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://mkjuergens.github.io/tags/bias-variance-decomposition/index.xml" rel="self" type="application/rss+xml"/><item><title>Epistemic uncertainty estimation methods are fundamentally incomplete</title><link>https://mkjuergens.github.io/research/position/</link><pubDate>Fri, 06 Feb 2026 00:00:00 +0000</pubDate><guid>https://mkjuergens.github.io/research/position/</guid><description>We argue that current epistemic uncertainty methods are fundamentally incomplete: unaccounted bias contaminates aleatoric estimates, and existing methods capture only partial variance contributions. — Machine Learning (Springer), 2025.</description></item></channel></rss>