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University of York
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Learning RGGs in probabilistic metric spaces reveals that edges can exist with a defined probability, transforming how we understand graph connectivity in complex datasets.
A dynamically updated Wishart prior for GP covariance matrices lets you diagnose uninformative inputs, potentially streamlining feature selection and improving model efficiency.
