<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Rust on Home</title><link>https://wamli.github.io/tags/rust/</link><description>Recent content in Rust 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/rust/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>From Math to Silicon: Implementing BLR+ARD with Rust and faer</title><link>https://wamli.github.io/blog/blr-implementation-rust-faer/</link><pubDate>Fri, 15 May 2026 00:00:00 +0000</pubDate><guid>https://wamli.github.io/blog/blr-implementation-rust-faer/</guid><description>The central theme is deceptively simple: never invert a matrix if you can avoid it. This post walks through the production Rust code that makes Bayesian Linear Regression with ARD efficient, numerically safe, and ready for embedded systems. Everything else follows from understanding why this principle matters.</description></item></channel></rss>