2 citations · 3 across the 12 of their papers we have counts for
6 papers · 1 filter
Stacking designs: designing multi-fidelity computer experiments with target predictive accuracy
Chih-Li Sung, Yi Ji, Simon Mak +2
In an era where scientific experiments can be very costly, multi-fidelity emulators provide a useful tool for cost-efficient predictive scientific computing. For scientific applica…
Efficient calibration for imperfect epidemic models with applications to the analysis of COVID-19
Chih-Li Sung, Ying Hung
The estimation of unknown parameters in simulations, also known as calibration, is crucial for practical management of epidemics and prediction of pandemic risk. A simple yet widel…
A clustered Gaussian process model for computer experiments
Chih-Li Sung, Benjamin Haaland, Youngdeok Hwang +1
A Gaussian process has been one of the important approaches for emulating computer simulations. However, the stationarity assumption for a Gaussian process and the intractability f…
Calibration for computer experiments with binary responses and application to cell adhesion study
Chih-Li Sung, Ying Hung, William Rittase +2
Calibration refers to the estimation of unknown parameters which are present in computer experiments but not available in physical experiments. An accurate estimation of these para…
Multi-Resolution Functional ANOVA for Large-Scale, Many-Input Computer Experiments
Chih-Li Sung, Wenjia Wang, Matthew Plumlee +1
The Gaussian process is a standard tool for building emulators for both deterministic and stochastic computer experiments. However, application of Gaussian process models is greatl…
A generalized Gaussian process model for computer experiments with binary time series
Chih-Li Sung, Ying Hung, William Rittase +2
Non-Gaussian observations such as binary responses are common in some computer experiments. Motivated by the analysis of a class of cell adhesion experiments, we introduce a genera…