Speaker: Dr. Won Chang Place: Online via Zoom, Zoom ID: 936 1134 3585 / Passcode: sunykorea Date and Time:
About the Speaker - Associate Professor in the Division of Statistics and Data Science at the Department of Mathematical Sciences University of Cincinnati
- Postdoctoral Scholar: University of Chicago, 2014-2016 - Ph.D.: Pennsylvania State University, 2014 (Statistics)
- M.S.: Korea University Seoul, 2009 (Statistics)
- B.S.: Korea University Seoul, 2007 (Statistics)
Abstract Computer models play a key role in many scientific and engineering problems. One major source of uncertainty in computer model experiments is input parameter uncertainty. Computer model calibration is a formal statistical procedure to infer input parameters by combining information from model runs and observational data. In this talk, I will introduce the basic framework for calibration and discuss how the input parameters can be estimated while accounting for data-model discrepancy using Bayesian inference. Some scientific applications including disease modeling and climate change projection will be discussed as well.