Technical Note: Approximate Bayesian parameterization of a process-based tropical forest model
Technical Note: Approximate Bayesian parameterization of a process-based tropical forest model
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DOI：
10.5194/bg-11-1261-2014
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发表时间：
2014-01-01
期刊：
影响因子：
4.9
通讯作者：
Huth, A.
中科院分区：
文献类型：
--
作者：
Hartig, F.;Dislich, C.;Huth, A.
基于过程的模型的逆参数估计是许多科学学科中的一个长期问题。逆参数估计的一个关键问题是如何定义量化模型预测与数据匹配程度的指标。这一指标可以用一般的成本或目标函数来表示，但统计反演方法需要一个特定的指标，即在给定模型参数的情况下观察数据的概率，称为似然函数。出于技术和计算原因，基于过程的随机模型的似然通常基于对观测数据的可变性的一般假设，而不是基于模型产生的随机性。直到最近几年，才有了新的方法，可以直接从随机模拟中产生可能性。这些近似贝叶斯方法以前的应用主要集中在相对简单的模型上。本文报告了基于模拟的似然近似在FORMIND中的应用，FORMIND是一个参数丰富的基于个体的热带森林动态模型。我们表明，基于传统马尔可夫链蒙特卡罗(MCMC)采样器中的参数似然近似的近似贝叶斯推理在从森林模型生成的虚拟清查数据中检索已知参数值方面表现良好。我们分析了参数估计的结果，检验了它对模型输出和观测数据(汇总统计)的选择和聚集的敏感性，并通过对厄瓜多尔热带森林的野外数据进行FORMIND模型的拟合来演示该方法的应用。最后，我们讨论了这种方法与另一种通常用于生成基于模拟的似然近似的方法-近似贝叶斯计算(ABC)的不同之处。结果表明，基于模拟的推理可以成功地应用于基于过程的高复杂性模型，相对于传统的逆参数估计方法具有相当大的概念优势。该方法特别适用于异质和复杂的数据结构，并且可以很容易地调整到其他模型类型，包括大多数随机总体和基于个体的模型。因此，我们的研究为基于随机过程的模型的参数估计提供了一种相当普遍的方法。
Inverse parameter estimation of process-based models is a long-standing problem in many scientific disciplines. A key question for inverse parameter estimation is how to define the metric that quantifies how well model predictions fit to the data. This metric can be expressed by general cost or objective functions, but statistical inversion methods require a particular metric, the probability of observing the data given the model parameters, known as the likelihood.For technical and computational reasons, likelihoods for process-based stochastic models are usually based on general assumptions about variability in the observed data, and not on the stochasticity generated by the model. Only in recent years have new methods become available that allow the generation of likelihoods directly from stochastic simulations. Previous applications of these approximate Bayesian methods have concentrated on relatively simple models. Here, we report on the application of a simulation-based likelihood approximation for FORMIND, a parameter-rich individual-based model of tropical forest dynamics.We show that approximate Bayesian inference, based on a parametric likelihood approximation placed in a conventional Markov chain Monte Carlo (MCMC) sampler, performs well in retrieving known parameter values from virtual inventory data generated by the forest model. We analyze the results of the parameter estimation, examine its sensitivity to the choice and aggregation of model outputs and observed data (summary statistics), and demonstrate the application of this method by fitting the FORMIND model to field data from an Ecuadorian tropical forest. Finally, we discuss how this approach differs from approximate Bayesian computation (ABC), another method commonly used to generate simulation-based likelihood approximations.Our results demonstrate that simulation-based inference, which offers considerable conceptual advantages over more traditional methods for inverse parameter estimation, can be successfully applied to process-based models of high complexity. The methodology is particularly suitable for heterogeneous and complex data structures and can easily be adjusted to other model types, including most stochastic population and individual-based models. Our study therefore provides a blueprint for a fairly general approach to parameter estimation of stochastic process-based models.