globalchange  > 气候变化与战略
DOI: 10.1111/ele.13728
论文题名:
Towards robust statistical inference for complex computer models
作者: Oberpriller J.; Cameron D.R.; Dietze M.C.; Hartig F.
刊名: Ecology Letters
ISSN: 1461023X
出版年: 2021
卷: 24, 期:6
起始页码: 1251
结束页码: 1261
语种: 英语
中文关键词: Bayesian Inference ; bias correction ; biased models ; data imbalance ; robust inference
英文关键词: calibration ; complexity ; computer simulation ; correction ; ecosystem response ; nonlinearity ; precision ; uncertainty analysis ; Bayes theorem ; computer simulation ; ecosystem ; forecasting ; statistical model ; uncertainty ; Bayes Theorem ; Computer Simulation ; Ecosystem ; Forecasting ; Models, Statistical ; Uncertainty
英文摘要: 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. © 2021 The Authors. Ecology Letters published by John Wiley & Sons Ltd.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/166903
Appears in Collections:气候变化与战略

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作者单位: Theoretical Ecology, University of Regensburg, Universitätsstraße 31, Regensburg, 93053, Germany; UK Centre for Ecology & Hydrology, Bush Estate, Penicuik, Midlothian EH260QB, United Kingdom; Department of Earth & Environment, Boston University, Boston, MA, United States

Recommended Citation:
Oberpriller J.,Cameron D.R.,Dietze M.C.,et al. Towards robust statistical inference for complex computer models[J]. Ecology Letters,2021-01-01,24(6)
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