Scalable spatio-temporal smoothing via hierarchical sparse Cholesky decomposition.
Scalable spatio-temporal smoothing via hierarchical sparse Cholesky decomposition.
复制标题
通过分层稀疏 Cholesky 分解进行可扩展的时空平滑。
DOI：
10.1002/env.2757
复制
发表时间：
2022
期刊：
影响因子：
1.7
通讯作者：
Jurek, M.
中科院分区：
文献类型：
--
作者：
Jurek, M.
我们提出了一个近似的前向滤波器后向采样器（FFBS）算法的大规模时空平滑。FFBS通常用于贝叶斯统计，当使用线性高斯状态空间模型时，但它需要反转具有潜在状态向量大小的协方差矩阵。与此操作相关的计算负担有效地阻止了其在高维设置中的应用。我们提出了一种基于高斯过程的分层Vecchia近似的可扩展时空FFBS方法，该方法先前已成功用于空间统计。在模拟和真实的数据上，我们的方法优于低秩FFBS近似。
We propose an approximation to the forward filter backward sampler (FFBS) algorithm for large‐scale spatio‐temporal smoothing. FFBS is commonly used in Bayesian statistics when working with linear Gaussian state‐space models, but it requires inverting covariance matrices which have the size of the latent state vector. The computational burden associated with this operation effectively prohibits its applications in high‐dimensional settings. We propose a scalable spatio‐temporal FFBS approach based on the hierarchical Vecchia approximation of Gaussian processes, which has been previously successfully used in spatial statistics. On simulated and real data, our approach outperformed a low‐rank FFBS approximation.