  Simulation models often have parameters as input and return outputs to
understand the behavior of complex systems. Calibration is the process of
estimating the values of the parameters in a simulation model in light of
observed data from the system that is being simulated. When simulation models
are expensive, emulators are built with simulation data as a computationally
efficient approximation of an expensive model. An emulator then can be used to
predict model outputs, instead of repeatedly running an expensive simulation
model during the calibration process. Sequential design with an intelligent
selection criterion can guide the process of collecting simulation data to
build an emulator, making the calibration process more efficient and effective.
This article proposes two novel criteria for sequentially acquiring new
simulation data in an active learning setting by considering uncertainties on
the posterior density of parameters. Analysis of several simulation experiments
and real-data simulation experiments from epidemiology demonstrates that
proposed approaches result in improved posterior and field predictions.
