BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Mike Pereira (Mines Paris - PSL University) DTSTART:20230223T121500Z DTEND:20230223T130000Z DTSTAMP:20260513T134150Z UID:gbgstats/15 DESCRIPTION:Title: Gaussian fields on Riemannian manifolds: Application to Geostatistics.\nby Mike Pereira (Mines Paris - PSL University) as part of Gothenburg s tatistics seminar\n\nLecture held in MVL15.\n\nAbstract\nMany applications in spatial statistics require data to be modeled by Gaussian processes on non-Euclidean domains\, or with non-stationary properties. Using such mo dels generally comes at the price of a drastic increase in operational cos ts (computational and storage-wise)\, rendering them hard to apply to larg e datasets. In this talk\, we propose a solution to this problem\, which r elies on the definition of a class of random fields on Riemannian manifold s. These fields extend ongoing work that has been done to leverage a chara cterization of the random fields classically used in Geostatistics as solu tions of stochastic partial differential equations. The discretization of these generalized random fields\, undertaken using a finite element approa ch\, then provides an explicit characterization that is leveraged to solve the scalability problem. Indeed\, matrix-free algorithms\, in the sense t hat they do not require to build and store any covariance (or precision) m atrix\, are derived to tackle for instance the simulation of large Gaussia n fields with given covariance properties\, even in the non-stationary set ting.\n LOCATION:https://researchseminars.org/talk/gbgstats/15/ END:VEVENT END:VCALENDAR