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Georgia Tech Statistics Seminar Series
Tuesday, November 19, 2013 at 12:00 noon
Executive classroom, ISyE Main Building
Calibration, Error, and Extrapolative Predictions with Computational Models
Dave Higdon
Scientist
Statistical Sciences Group
Los Alamos National Laboratory
Abstract: In the presence of relevant physical observations, one can usually calibrate a computer model, and even estimate systematic discrepancies of the model from reality. Estimating and quantifying the uncertainty in this model discrepancy can lead to reliable predictions - so long as the prediction "is similar to" the available physical observations. Exactly how to define "similar" has proven difficult in many applications. Clearly it depends on how well the computational model captures the relevant physics in the system, as well as how portable the model discrepancy is in going from the available physical data to the prediction. This talk will discuss these concepts using computational models ranging from simple to very complex.
Short Bio: Dr. Higdon is a scientist in the statistical sciences group at the Los Alamos National Laboratory, Los Alamos, NM. He served as the group leader from 2005-2010. His research interests are in inference from combining computer simulation models with data from physical experiments or observations; modeling non-standard dependence structure; spatial modeling; inverse problems; image modeling; multiscale models; Monte Carlo methods; and applications in environmental and physical sciences. He received the Jack Youden prize from ASQ in 2006. He has started the ASA interest group on uncertainty quantification in 2010 and has served in the editorial board of several statistical journals.
