时 间：2019年5月21日(周二)上午10:00-11:00
地 点：中北校区理科大楼A1716会议室
题 目：Emulation of computer models:Models with massive output and coupled models
报告人：James O. Berger （Duke University）
摘 要：
Often computer models yield massive output; e.g., a weather model will yield the predicted temperature over a huge grid of points in space and time. Emulation of a computer model is the process of finding an approximation to the computer model that is much faster to run than the computer model itself (which can often take hours or days for a single run). Many successful emulation approaches are statistical in nature, but these have only rarely attempted to deal with massive computer model output; some approaches that have been tried include utilization of multivariate emulators, modeling of the output (e.g., through some basis representation, including PCA), and construction of parallel emulators at each grid point, with the methodology typically based on use of Gaussian processes to construct the approximations. These approaches will be reviewed, with the startling computational simplicity with which the last approach can be implemented being highlighted and its remarkable success being illustrated and explained; in particular, the surprising fact that one can ignore spatial structure in the massive output is explained. All results will be illustrated with a computer model of volcanic pyroclastic flow, the goal being the prediction of hazard probabilities near active volcanoes. As time permits, work on emulating coupled computer models (e.g., the computer model of pyroclastic flow, coupled with a computer model assessing damage to buildings from such flows) will be discussed.
报告人简介：
James O. Berger, the Arts and Sciences Professor of Statistics, Duke University, USA. Honorary Professor of Statistics, East China Normal University, China. Among the awards and honors Berger has received are Guggenheim and Sloan fellowships, the COPSS President's Award in 1985, election as foreign member of the Spanish Real Academia de Ciencias in 2002, and election to the U.S. National Academy of Sciences in 2003, “Most Influential Bayesian Analysis paper” in the first 10 years of the journal in 2016.