论文标题
贝叶斯对北极海冰时空变化的推断
Bayesian Inference of Spatio-Temporal Changes of Arctic Sea Ice
论文作者
论文摘要
由于其迅速下降，北极海冰范围引起了地球科学家的兴趣和警报。在本文中，我们提出了一个贝叶斯时空层次统计模型，用于二进制北极海冰数据，其中二十年来，使用了潜在的动态时空高斯过程，用于通过logit链路函数来建模数据依赖性。我们的最终目标是推断二十年来北极海冰的动态空间行为。使用自体诊断进行评估，以身体动机的协变量进行评估。我们的贝叶斯时空模型显示了这种复杂的层次模型中的参数不确定性如何影响时空预测。提出了新的摘要统计数据的后验分布，以检测自1997年以来二十年来北极海冰的变化。
Arctic sea ice extent has drawn increasing interest and alarm from geoscientists, owing to its rapid decline. In this article, we propose a Bayesian spatio-temporal hierarchical statistical model for binary Arctic sea ice data over two decades, where a latent dynamic spatio-temporal Gaussian process is used to model the data-dependence through a logit link function. Our ultimate goal is to perform inference on the dynamic spatial behavior of Arctic sea ice over a period of two decades. Physically motivated covariates are assessed using autologistic diagnostics. Our Bayesian spatio-temporal model shows how parameter uncertainty in such a complex hierarchical model can influence spatio-temporal prediction. The posterior distributions of new summary statistics are proposed to detect the changing patterns of Arctic sea ice over two decades since 1997.
