Bayesian Additive Regression Tree Calibration of Complex High-Dimensional Computer Models

Our approach leverages recent ideas from Bayesian additive regression Tree models to construct a random basis representation of the simulator outputs and observational data. The approach can flexibly handle high-dimensional datasets, high-dimensional simulator inputs, and calibration parameters while quantifying important sources of uncertainty in the resulting inference. We demonstrate our methodology on a CO 2 emissions rate calibration problem, and on a complex simulator of subterranean radionuclide dispersion, which simulates the spatial–temporal diffusion of radionuclides released during nuclear bomb tests at the Nevada Test Site.

Supplementary computer code and datasets are available online.