<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Gaussian processes on Stuart Lacy</title><link>/tags/gaussian-processes/</link><description>Recent content in Gaussian processes on Stuart Lacy</description><generator>Hugo -- gohugo.io</generator><language>en-uk</language><copyright>&amp;copy; 2023 Stuart Lacy</copyright><lastBuildDate>Mon, 26 Oct 2026 00:00:00 +0000</lastBuildDate><atom:link href="/tags/gaussian-processes/index.xml" rel="self" type="application/rss+xml"/><item><title>Modelling running fitness and predicting race times: an AI-assisted investigation</title><link>/2026/10/26/modelling-running-fitness-and-predicting-race-times-an-ai-assisted-investigation/</link><pubDate>Mon, 26 Oct 2026 00:00:00 +0000</pubDate><guid>/2026/10/26/modelling-running-fitness-and-predicting-race-times-an-ai-assisted-investigation/</guid><description>Motivation Since I started my own running data dashboard, I&amp;rsquo;ve been gradually adding my own custom features that I find useful for my training objectives. One bit of analysis I&amp;rsquo;ve always wanted to do is model my fitness with the overarching objective of generating accurate race time predictions (or at least more accurate than Garmin&amp;rsquo;s which I&amp;rsquo;m wildly sceptical of).
Modelling fitness isn&amp;rsquo;t too hard - you can simply look at your pace per heart rate for a rough guide - however, predicting race times is a lot more involved.</description></item></channel></rss>