Optimising the Unknown: A Bayesian Approach to Networked Systems
Bayesian optimisation is a powerful machine learning technique for efficiently finding the optimum of expensive, black-box functions. A new study by Li, Sanz-Alonso, and Yang extends this framework to the complex, interconnected domains of networks. The research presents a principled methodology for performing optimisation when the underlying variables or parameters are structured as a graph, a scenario common in modelling social, biological, and technological systems where relationships and dependencies are key.
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