Taking error into account when fitting models using Approximate Bayesian Computation.

Taking error into account when fitting models using Approximate Bayesian Computation.
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使用近似贝叶斯计算拟合模型时考虑误差。

DOI:
10.1002/eap.1656
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发表时间:
2018
期刊:
a publication of the Ecological Society of America
影响因子:
--
通讯作者:
Van Der Vaart E
Van Der Vaart E
中科院分区:
--
文献类型:
--
作者:
Van Der Vaart E

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随机计算机模拟通常是回答与生态管理有关的问题的唯一实用方法。然而,由于其复杂性,这些模型很难校准和评估。近似贝叶斯计算(ABC)提供了一个越来越流行的方法来解决这个问题,广泛应用于各种领域。然而,确保ABC估计的准确性一直很困难。在这里,我们通过将误差估计纳入ABC协议来获得更准确的估计。我们展示了如何可以做到这一点的数据组成的重复测量相同的数量和错误可以被假定为正态分布和独立的。然后,我们推导出正确的接受概率的概率ABC算法,并更新的覆盖测试的准确性进行评估。我们将这种方法(我们称之为误差校准ABC)应用于一个玩具示例和一个用于环境风险评估的蚯蚓的现实14参数模拟模型。与精确方法的比较和诊断覆盖测试表明,我们的方法改进了两个模型参数值及其可信区间的估计。
Stochastic computer simulations are often the only practical way of answering questions relating to ecological management. However, due to their complexity, such models are difficult to calibrate and evaluate. Approximate Bayesian Computation (ABC) offers an increasingly popular approach to this problem, widely applied across a variety of fields. However, ensuring the accuracy of ABC's estimates has been difficult. Here, we obtain more accurate estimates by incorporating estimation of error into the ABC protocol. We show how this can be done where the data consist of repeated measures of the same quantity and errors may be assumed to be normally distributed and independent. We then derive the correct acceptance probabilities for a probabilistic ABC algorithm, and update the coverage test with which accuracy is assessed. We apply this method, which we call error‐calibrated ABC, to a toy example and a realistic 14‐parameter simulation model of earthworms that is used in environmental risk assessment. A comparison with exact methods and the diagnostic coverage test show that our approach improves estimation of parameter values and their credible intervals for both models.
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