Premium
Towards robust statistical inference for complex computer models
Author(s) -
Oberpriller Johannes,
Cameron David R.,
Dietze Michael C.,
Hartig Florian
Publication year - 2021
Publication title -
ecology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.852
H-Index - 265
eISSN - 1461-0248
pISSN - 1461-023X
DOI - 10.1111/ele.13728
Subject(s) - inference , computer science , calibration , statistical inference , task (project management) , complex system , nonlinear system , data mining , machine learning , artificial intelligence , ecology , econometrics , statistics , mathematics , engineering , physics , systems engineering , quantum mechanics , biology
Ecologists increasingly rely on complex computer simulations to forecast ecological systems. To make such forecasts precise, uncertainties in model parameters and structure must be reduced and correctly propagated to model outputs. Naively using standard statistical techniques for this task, however, can lead to bias and underestimation of uncertainties in parameters and predictions. Here, we explain why these problems occur and propose a framework for robust inference with complex computer simulations. After having identified that model error is more consequential in complex computer simulations, due to their more pronounced nonlinearity and interconnectedness, we discuss as possible solutions data rebalancing and adding bias corrections on model outputs or processes during or after the calibration procedure. We illustrate the methods in a case study, using a dynamic vegetation model. We conclude that developing better methods for robust inference of complex computer simulations is vital for generating reliable predictions of ecosystem responses.