高斯工艺（GPS）是高度表达的概率模型。一个主要的限制是他们的计算复杂性。天真，精确的GP推理需要$ \ MATHCAL {o}（n^3）$计算$ n $表示建模点的数量。当前克服此限制的方法分别依赖于数据或内核的稀疏，结构化或随机表示形式，并且通常涉及嵌套的优化以评估GP。我们提出了一种名为迭代图表改进（ICR）的新的，生成的方法，以在$ \ Mathcal {o}（n）$ ntive ntime ntime ntime ntake ntige内核中衰减内核，而无需嵌套优化的时间。 ICR通过将不同分辨率的建模位置的视图与用户提供的坐标图组合在一起，代表长期和短距离相关性。在我们对两个数量级的间距有所不同的点的实验中，ICR的准确性与最新的GP方法相当。 ICR在CPU和GPU上以一个数量级的计算速度来优于现有方法，并且已经成功地应用于具有122亿美元参数的GP模型。

The viral load of patients infected with SARS-CoV-2 varies on logarithmic scales and possibly with age. Controversial claims have been made in the literature regarding whether the viral load distribution actually depends on the age of the patients. Such a dependence would have implications for the COVID-19 spreading mechanism, the age-dependent immune system reaction, and thus for policymaking. We hereby develop a method to analyze viral-load distribution data as a function of the patients' age within a flexible, non-parametric, hierarchical, Bayesian, and causal model. The causal nature of the developed reconstruction additionally allows to test for bias in the data. This could be due to, e.g., bias in patient-testing and data collection or systematic errors in the measurement of the viral load. We perform these tests by calculating the Bayesian evidence for each implied possible causal direction. The possibility of testing for bias in data collection and identifying causal directions can be very useful in other contexts as well. For this reason we make our model freely available. When applied to publicly available age and SARS-CoV-2 viral load data, we find a statistically significant increase in the viral load with age, but only for one of the two analyzed datasets. If we consider this dataset, and based on the current understanding of viral load's impact on patients' infectivity, we expect a non-negligible difference in the infectivity of different age groups. This difference is nonetheless too small to justify considering any age group as noninfectious.