The Graduate Preparation Program (GPP) is a four-week intensive non-credit course offered in person on the main campus prior to the fall quarter, which is open to all current and newly admitted international graduate students. It focuses on English for Academic Purposes (EAP), academic skills, and cultural orientation. The program also provides a foundation for […]
Spatially dependent data are common in environmental and climate studies, where Gaussian process models are used for prediction and uncertainty quantification. Learning their covariance structure involves both statistical and computational challenges. Likelihood-based inference may be sensitive to covariance misspecification, while repeated evaluation of prediction-oriented criteria can be computationally demanding. To study these issues, we develop […]