Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1111/ele.13728 |
Towards robust statistical inference for complex computer models | |
Johannes Oberpriller; David R. Cameron; Michael C. Dietze; Florian Hartig | |
2021-03-30 | |
发表期刊 | Ecology Letters |
出版年 | 2021 |
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. |
领域 | 资源环境 |
URL | 查看原文 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/321001 |
专题 | 资源环境科学 |
推荐引用方式 GB/T 7714 | Johannes Oberpriller,David R. Cameron,Michael C. Dietze,et al. Towards robust statistical inference for complex computer models[J]. Ecology Letters,2021. |
APA | Johannes Oberpriller,David R. Cameron,Michael C. Dietze,&Florian Hartig.(2021).Towards robust statistical inference for complex computer models.Ecology Letters. |
MLA | Johannes Oberpriller,et al."Towards robust statistical inference for complex computer models".Ecology Letters (2021). |
条目包含的文件 | 条目无相关文件。 |
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