Bayesian uncertainty analysis represents a powerful statistical framework that integrates prior knowledge with observed measurement data to quantify uncertainty in a consistent probabilistic manner.

We consider quasilikelihood models when some of the predictors are measured with error. In many cases, the true but fallible predictor is impossible to measure, and ...

Generalized Efficiency Measures (GEMS) for use in DEA are developed and analyzed in a context of differing models where they might be employed. The additive model of DEA is accorded a central role and ...

This course is available on the Global MSc in Management, Global MSc in Management (CEMS MIM), Global MSc in Management (MBA Exchange), MSc in Applied Social Data Science, MSc in European and ...

An analysis by Epoch AI, a nonprofit AI research institute, suggests the AI industry may not be able to eke massive performance gains out of reasoning AI models for much longer. As soon as within a ...

The modeling industry has long been notorious for its limiting requirements for model measurements. If a woman wasn’t a 5’10” waif, the chances of breaking into the biz were slim to none. This is ...

The most exciting possible avenue for physics beyond the standard model of particle physics has been, for years, the discrepancy in the anomalous magnetic moment of the muon. After refinement in the ...