<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Python on Local Optima</title><link>https://anhosu.com/tags/python/</link><description>Recent content in Python on Local Optima</description><generator>Hugo</generator><language>en-us</language><copyright>© {year} Anders E. Nielsen</copyright><lastBuildDate>Wed, 21 Oct 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://anhosu.com/tags/python/index.xml" rel="self" type="application/rss+xml"/><item><title>Bayesian Optimisation from Scratch in Python</title><link>https://anhosu.com/post/bayesian-optimisation-from-scratch-python/</link><pubDate>Wed, 21 Oct 2026 00:00:00 +0000</pubDate><guid>https://anhosu.com/post/bayesian-optimisation-from-scratch-python/</guid><description>&lt;p&gt;Bayesian optimisation is a powerful technique for optimising expensive functions or processes. In many applications, such as drug discovery, manufacturing, machine learning, or scientific experimentation, the function or process to be optimised may be time-consuming or costly to evaluate. Bayesian optimisation provides a framework for sequential experimentation and for finding optima with as few evaluations as possible.&lt;/p&gt;</description></item></channel></rss>