论文标题
通过分层稀疏cholesky分解可扩展时空平滑
Scalable Spatio-Temporal Smoothing via Hierarchical Sparse Cholesky Decomposition
论文作者
论文摘要
我们提出了针对大规模时空平滑的前向滤波器 - 背离传采采样器（FFBS）算法的近似值。 FFBS在使用线性高斯州空间模型时通常在贝叶斯统计中使用，但是它需要具有具有潜在状态向量大小的倒数协方差矩阵。与此操作相关的计算负担有效地禁止其在高维环境中的应用。我们提出了一种基于高斯过程的层次）近似的可扩展时空FFBS方法，该方法先前已成功地用于空间统计。在模拟和真实数据上，我们的方法的表现优于低级FFB的近似。
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.
