Professor Jim Berger
Jim Berger is the Arts and Sciences Professor of Statistics at Duke University. He was president of the Institute of Mathematical Statistics from 1995-1996 and of the International Society for Bayesian Analysis during 2004. He was the founding director of the Statistical and Applied Mathematical Sciences Institute, serving from 2002-2010. He was co-editor of the Annals of Statistics from 1998-2000 and was a founding editor of the Journal on Uncertainty Quantification in 2012. Berger is a Fellow of the ASA and the IMS and has received Guggenheim and Sloan Fellowships. He received the Committee of Presidents of Statistical Societies ‘President's Award’ in 1985, was the COPSS Fisher Lecturer in 2001 and the Wald Lecturer of the IMS in 2007. He was elected as a foreign member of the Spanish Real Academia de Ciencias in 2002, elected to the USA National Academy of Sciences in 2003, was awarded an honorary Doctor of Science degree from Purdue University in 2004, and became an Honorary Professor at East China Normal University in 2011. He has directed 34 Ph.D. students, written or edited 16 books, and published over 180 papers
Address:Department of Statistical ScienceDuke University Durham, NC 27708-0251, USA
Title: Gaussian Process Emulation of Computer Models with Massive Output
Abstract: 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). Most 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. All results will be illustrated with a computer model of volcanic pyroclastic flow, the goal being the prediction of hazard probabilities near active volcanoes.
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