AI RESEARCH
Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial
arXiv CS.LG
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ArXi:2604.01328v1 Announce Type: new Traditional scientific discovery relies on an iterative hypothesise-experiment-refine cycle that has driven progress for centuries, but its intuitive, ad-hoc implementation often wastes resources, yields inefficient designs, and misses critical insights. This tutorial presents Bayesian Optimisation (BO), a principled probability-driven framework that formalises and automates this core scientific cycle.