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首页> 外文期刊>Ecological Modelling >Evaluation of a soil greenhouse gas emission model based on Bayesian inference and MCMC: Parameter identifiability and equifinality
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Evaluation of a soil greenhouse gas emission model based on Bayesian inference and MCMC: Parameter identifiability and equifinality

机译：基于贝叶斯推断和MCMC的土壤温室气体排放模型评估：参数可识别性和均等性

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摘要

Identifiability and equifinality are two interrelated concepts in mathematical modeling. The derivation of the Hessian matrix becomes crucial when the condition number is used as a diagnostic indicator for identifiability. The covariance-inverse (CI) method was proposed to derive the Hessian matrix via the inverse matrix of covariance. The covariance matrix is calculated directly from the posterior parameter samples. Compared with two existing methods, i.e., difference quotients (DQ) and quasi-analytical (QA), CI is more efficient and reliable. The CI method was then used for identifiability diagnosis on a soil greenhouse gas emission (SoilGHG) model. The model as a whole was poorly identified, but a reduced model with fewer parameters could become identifiable, which is called “conditionally identifiable” in this paper. The geometric mean condition numbers in terms of sorted singular values of the full Hessian matrix could be adopted as criteria to determine at most how many undetermined parameters might be included in an identifiable or weakly identifiable model. The combinations of parameters that made the model identifiable were also determined by the proposed diagnosis method. We addressed the importance of understanding both identifiability and equifinality in ecosystem modeling.
机译：可识别性和相等性是数学建模中两个相互关联的概念。当条件编号用作可识别性的诊断指标时，Hessian矩阵的推导至关重要。提出了协方差逆（CI）方法，通过协方差逆矩阵推导Hessian矩阵。直接从后验参数样本计算协方差矩阵。与现有的两种方法即差商（DQ）和准分析（QA）相比，CI更加有效和可靠。然后，将CI方法用于土壤温室气体排放（SoilGHG）模型的可识别性诊断。整个模型的识别性很差，但是具有较少参数的简化模型可以识别，在本文中称为“有条件可识别”。可以采用根据完整的Hessian矩阵的奇异值排序的几何平均条件数作为确定最多可包含多少个未确定参数的标准，该参数可以包含在可识别模型或弱可识别模型中。通过所提出的诊断方法，还可以确定使模型可识别的参数组合。我们谈到了在生态系统建模中理解可识别性和等同性的重要性。

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