Dimension Reduction

Tuesday, April 24, 2018 - 1:00pm - 1:30pm
Oksana Chkrebtii (The Ohio State University)
When computational constraints prohibit model evaluation at all but a small number of parameter settings, a dimension-reduced emulator of the system can be constructed and interrogated at arbitrary parameter regimes. Existing approaches to emulation consider models with deterministic output. However, in many cases the underlying mathematical model, or the simulator approximating the mathematical model, are stochastic. We propose a Bayesian calibration approach for stochastic simulators.
Wednesday, February 21, 2018 - 2:00pm - 2:40pm
Tucker McElroy (U.S. Bureau of the Census)
We develop statistical tools for time series analysis of high-dimensional multivariate datasets, when a few core series are of principal interest and there are many potential ancillary predictive variables. The
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