Statistical Models and Methods Based on Stochastic Partial Differential Equations David Bolin, Associate Professor, Statistics Mar 23, 14:00 - 15:30 B9 L2 R2322; Zoom Meeting ID 93209452060 SPDEs non-Gaussian random fields statistical analysis Metric graphs This talk presents an overview of our research on statistical methods using stochastic partial differential equations (SPDEs), focusing on non-Gaussian random fields and fractional-order SPDEs, and theory for random fields and statistical analysis on metric graphs, highlighting theoretical contributions, software development, and applications relevant to KAUST RDI pillars.
The KAUST 2022 Workshop on Statistics Oct 31, 09:00 - Nov 3, 17:00 B2 B3 A0215 statistics data science big data modeling complex data The workshop will cover current trends in statistics and the statistical modeling of big and complex data. Over four days, it will have talks by leading experts as well as two poster sessions where students and postdocs will present their work. All talks will be given Auditorium 0215, between Buildings 2 and 3 at KAUST. Speakers Aritz Adin (Public University of Navarre) Ahmadou Alioum (Bordeaux School of Public Health) Denis Allard (INRAE) Vera Baladandayupathani (University of Michigan) Emily Hector (North Carolina State University) Eduardo Garcia Portugues (Carlos III University of Madrid)
A new class of random field models for data on networks David Bolin, Associate Professor, Statistics Oct 3, 12:00 - 13:00 B9 L2 R2322 H1 applied statistics machine learning Gaussian processes Random fields are popular models in statistics and machine learning for spatially dependent data on Euclidian domains. However, in many applications, data is observed on non-Euclidian domains such as street networks. In this case, it is much more difficult to construct valid random field models. In this talk, we discuss some recent approaches to modeling data in this setting, and in particular define a new class of Gaussian processes on compact metric graphs.
Proper scoring rules and model selection for stochastic processes David Bolin, Associate Professor, Statistics Feb 11, 12:00 - 13:00 KAUST In this talk, we begin by a brief introduction to proper scoring rules and their use in statistics. Then, we discuss an often overlooked problem: the up-weighting of observations with large uncertainty, which can lead to unintuitive rankings of models, by some of the most popular proper scoring rules, such as the continuously ranked probability score (CRPS), the MAE, and the MSE.
