Istem Fer, Elizabeth Cowdery, and Mike Dietze publish in Biogeosciences
Postdoc Istem Fer, PhD candidate Elizabeth Cowdery, and Associate Professor Mike Dietze have co-authored “Linking big models to big data: efficient ecosystem model calibration through Bayesian model emulation” in Biogeosciences. “Bayesian methods provide a rigorous data assimilation framework for these applications, especially for problems with multiple data constraints,” the authors note. “However, the Markov chain Monte Carlo (MCMC) techniques underlying most Bayesian calibration can be prohibitive for computationally demanding models and large datasets. We employ an alternative method, Bayesian model emulation of sufficient statistics, that can approximate the full joint posterior density, is more amenable to parallelization, and provides an estimate of parameter sensitivity.”