The computational order of a DACE dynamical model

Abstract

Multivariate design and analysis of computer experiments (DACE) methodology can be useful in situations where a dynamical computer model produces time series data sets. The main result of this paper determines the computational order of prediction from a dynamical statistical model underpinned by the dynamical computer model. Furthermore, it is shown that the computational orders of predictions from this dynamical statistical model and from a black box statistical model are comparable, but the likelihood optimization of the former model is more efficient. A virus dynamics example shows that the dynamical statistical model predictions can be more accurate than both the black box statistical model predictions and a coarse numerical solution of similar computational order.