通过自回归逻辑-β斯特林-伽马过程进行动态污染簇的贝叶斯非参数建模
Bayesian nonparametric modeling of dynamic pollution clusters through an autoregressive logistic-beta Stirling-gamma process
AI总结：
本文提出一种基于逻辑-β依赖DP和斯特林-伽马先验的动态聚类方法，用于高效建模动态污染簇，适用于实际环境数据应用。
摘要：
细颗粒物（FSP），通常称为PM2.5，是危害最大的空气污染物之一，对人口健康和环境完整性构成严重风险。因此，准确识别FSP的潜在簇对于有效的空气质量管理和公共卫生管理至关重要。然而，这一任务尤为复杂，因为FSP簇可能依赖于各种区域和时间因素，这些因素应在建模过程中加以考虑。因此，我们利用了贝叶斯非参数动态聚类思想，其中聚类结构可能受复杂依赖关系的影响。然而，现有的动态聚类实现依赖于基于copula的依赖狄利克雷过程（DPs），在实际应用中带来了相当大的计算挑战。为此，我们提出了一种更高效的动态聚类替代方法，通过引入逻辑-β依赖DP的新思想。我们还采用了一种新的分布族斯特林-伽马先验，作为我们基础DP的浓度参数的先验，使将先验知识纳入模型的过程更加简便。此外，我们还提出了高效的后验推断计算策略。我们将所提出的方法应用于智利的动态FSP簇识别，并展示了其在现有方法上的优越性能。
英文摘要：
Fine suspended particulates (FSP), commonly known as PM2.5, are among the most harmful air pollutants, posing serious risks to population health and environmental integrity. As such, accurately identifying clusters of air-quality monitoring stations exhibiting similar FSP levels is essential for guiding targeted interventions and localized public-health response measures. This task, however, is notably nontrivial as FSP levels may depend on various regional and temporal factors, which should be incorporated in the modeling process. Thus, we capitalize on Bayesian nonparametric dynamic clustering ideas, in which clustering structures may be influenced by complex dependencies. Existing implementations of dynamic clustering, however, rely on copula-based dependent Dirichlet processes (DPs), presenting considerable computational challenges for real-world deployment. With this in mind, we propose a more efficient alternative for dynamic clustering by incorporating the novel ideas of logistic-beta dependent DPs. We also adopt a Stirling-gamma prior--a novel distribution family--on the concentration parameter of our underlying DP, easing the process of incorporating prior knowledge into the model. Efficient computational strategies for posterior inference are also presented. We apply our proposed method to identify dynamic clusters of air-quality monitoring stations across Chile and demonstrate its superior performance over existing approaches.
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