Correlations between parameters in risk models: Estimation and propagation of uncertainty by Markov Chain Monte Carlo
Correlations between parameters in risk models: Estimation and propagation of uncertainty by Markov Chain Monte Carlo
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DOI：
10.1111/j.0272-4332.2003.00386.x
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发表时间：
2003-12-01
期刊：
影响因子：
3.8
通讯作者：
Lu, G
中科院分区：
文献类型：
--
作者：
Ades, AE;Lu, G
蒙特卡罗模拟已成为通过风险模型传播参数不确定性的公认方法。然而，人们普遍认识到，如果模型要对风险的不确定性作出正确的评估，就必须考虑输入变量之间的相关性。已经提出了各种两阶段方法，首先估计相关结构，然后生成蒙特卡罗模拟，其中包含该结构，同时保持参数的边际分布不变。在这里，我们提出了一种单阶段替代方案，其中通过贝叶斯马尔可夫链蒙特卡罗方法直接从数据中估计相关结构。在给定数据和模型的情况下，输出后验分布的样本正确地反映了参数之间的相关性。除了计算简单之外，这种方法利用了来自各种结构的现有证据，包括不完整的数据和相关和不相关的重复观测。贝叶斯方法的主要优点是，它没有假设相关结构是固定和已知的，而是捕获了所有参数（包括方差和协方差）中数据引起的联合不确定性，并通过决策或风险模型正确地传播这种不确定性。以固体废物焚烧炉排放二恶英同系物为例说明这些特点。
Monte Carlo simulation has become the accepted method for propagating parameter uncertainty through risk models. It is widely appreciated, however, that correlations between input variables must be taken into account if models are to deliver correct assessments of uncertainty in risk. Various two-stage methods have been proposed that first estimate a correlation structure and then generate Monte Carlo simulations, which incorporate this structure while leaving marginal distributions of parameters unchanged. Here we propose a one-stage alternative, in which the correlation structure is estimated from the data directly by Bayesian Markov Chain Monte Carlo methods. Samples from the posterior distribution of the outputs then correctly reflect the correlation between parameters, given the data and the model. Besides its computational simplicity, this approach utilizes the available evidence from a wide variety of structures, including incomplete data and correlated and uncorrelated repeat observations. The major advantage of a Bayesian approach is that, rather than assuming the correlation structure is fixed and known, it captures the joint uncertainty induced by the data in all parameters, including variances and covariances, and correctly propagates this through the decision or risk model. These features are illustrated with examples on emissions of dioxin congeners from solid waste incinerators.