<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Uncertainty-Quantification on Home</title><link>https://wamli.github.io/tags/uncertainty-quantification/</link><description>Recent content in Uncertainty-Quantification on Home</description><generator>Hugo</generator><language>en-US</language><copyright>&amp;copy; 2026 Christoph Brewing</copyright><lastBuildDate>Sat, 16 May 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://wamli.github.io/tags/uncertainty-quantification/index.xml" rel="self" type="application/rss+xml"/><item><title>Bayesian Linear Regression for Sensor Calibration</title><link>https://wamli.github.io/blog/blr-sensor-calibration-series/</link><pubDate>Fri, 15 May 2026 00:00:00 +0000</pubDate><guid>https://wamli.github.io/blog/blr-sensor-calibration-series/</guid><description>A comprehensive two-part series on building principled, uncertainty-quantified sensor calibration systems using Bayesian Linear Regression with Automatic Relevance Determination. Theory, implementation, and real-world patterns.</description></item><item><title>When Your Sensor Knows What It Doesn't Know</title><link>https://wamli.github.io/blog/blr-and-ard/</link><pubDate>Fri, 15 May 2026 00:00:00 +0000</pubDate><guid>https://wamli.github.io/blog/blr-and-ard/</guid><description>Bayesian Linear Regression with Automatic Relevance Determination combines principled uncertainty quantification, automatic feature selection, and a closed-form solution—all from one coherent mathematical framework. This post explains how and why.</description></item></channel></rss>