Implicit Estimation of Ecological Model Parameters
Implicit Estimation of Ecological Model Parameters
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DOI：
10.1007/s11538-012-9801-6
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发表时间：
2013-01
影响因子：
3.5
通讯作者：
B. Weir;Robert N. Miller;Y. Spitz
中科院分区：
文献类型：
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作者：
B. Weir;Robert N. Miller;Y. Spitz
我们引入了一种用于状态和参数估计的隐式方法，并将其应用于随机生态模型。该方法使用粒子集合来近似基于状态噪声观测的模型解和参数的分布。对于每个粒子，它首先根据观察结果确定可能的值，然后围绕这些值进行采样。该方法具有很强的理论基础，适用于非线性模型和非高斯分布，可以估计任意数量的模型参数、初始条件和模型误差协方差。该方法称为隐式方法，因为它更新粒子而不形成正向模型积分的预测分布。作为不同同化技术的比较点，我们考虑一个或多个分叉将真实参数与其初始近似值分开的示例。隐式估计量是渐近无偏的，其均方根误差与其他方法相当或小于其他方法，并且即使在集合规模较小的情况下也是准确的。
We introduce an implicit method for state and parameter estimation and apply it to a stochastic ecological model. The method uses an ensemble of particles to approximate the distribution of model solutions and parameters conditioned on noisy observations of the state. For each particle, it first determines likely values based on the observations, then samples around those values. This approach has a strong theoretical foundation, applies to nonlinear models and non-Gaussian distributions, and can estimate any number of model parameters, initial conditions, and model error covariances. The method is called implicit because it updates the particles without forming a predictive distribution of forward model integrations. As a point of comparison for different assimilation techniques, we consider examples in which one or more bifurcations separate the true parameter from its initial approximation. The implicit estimator is asymptotically unbiased, has a root-mean-squared error comparable to or less than the other methods, and is accurate even with small ensemble sizes.