On the number of Monte Carlo runs in comparative probabilistic LCA
On the number of Monte Carlo runs in comparative probabilistic LCA
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DOI：
10.1007/s11367-019-01698-4
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发表时间：
2019-10-22
影响因子：
4.8
通讯作者：
Heijungs, Reinout
中科院分区：
文献类型：
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作者：
Heijungs, Reinout
介绍蒙特卡罗技术被广泛使用，并建议包括不确定性LCA。通常情况下，运行1000或10，000次，但该数量没有明确的参数，并且随着LCA数据库的规模不断增长，运行次数过多可能会非常耗时。因此，我们调查，如果大量的运行是有用的，或者如果它可能是不必要的，甚至是有害的。概率论我们审查的标准理论或概率分布描述随机变量，包括不同的随机变量的组合成一个计算。我们还回顾了标准理论的推断统计估计的概率分布，给定的样本值。为了估计概率分布函数的分布，有两种主要的技术可用，即应用概率论的分析技术和使用蒙特卡罗模拟的数值技术。由于分析技术往往是不可用的，显然的出路是蒙特卡罗。然而，我们证明和说明，它会导致过于精确的估计参数的值的结论，和不正确的假设tests.Numerical explectionWe证明了两个简单的情况下的效果：一个系统中的一个独立的分析和两个替代系统的比较分析。这两种情况都说明，不应该被拒绝的统计假设，事实上是拒绝在一个高度令人信服的方式，从而指出了一个根本的缺陷。讨论和conclusionsApart的明显建议，使用较大的样本估计输入分布，我们建议限制的Monte Carlo运行的数量不大于用于输入参数的样本大小。最后要注意的是，当输入参数不是使用样本估计的，而是通过一个过程，如流行的谱系方法，蒙特卡罗方法不应该使用。
IntroductionThe Monte Carlo technique is widely used and recommended for including uncertainties LCA. Typically, 1000 or 10,000 runs are done, but a clear argument for that number is not available, and with the growing size of LCA databases, an excessively high number of runs may be a time-consuming thing. We therefore investigate if a large number of runs are useful, or if it might be unnecessary or even harmful.Probability theoryWe review the standard theory or probability distributions for describing stochastic variables, including the combination of different stochastic variables into a calculation. We also review the standard theory of inferential statistics for estimating a probability distribution, given a sample of values. For estimating the distribution of a function of probability distributions, two major techniques are available, analytical, applying probability theory and numerical, using Monte Carlo simulation. Because the analytical technique is often unavailable, the obvious way-out is Monte Carlo. However, we demonstrate and illustrate that it leads to overly precise conclusions on the values of estimated parameters, and to incorrect hypothesis tests.Numerical illustrationWe demonstrate the effect for two simple cases: one system in a stand-alone analysis and a comparative analysis of two alternative systems. Both cases illustrate that statistical hypotheses that should not be rejected in fact are rejected in a highly convincing way, thus pointing out a fundamental flaw.Discussion and conclusionsApart form the obvious recommendation to use larger samples for estimating input distributions, we suggest to restrict the number of Monte Carlo runs to a number not greater than the sample sizes used for the input parameters. As a final note, when the input parameters are not estimated using samples, but through a procedure, such as the popular pedigree approach, the Monte Carlo approach should not be used at all.