Ensemble Kalman Methods for High-Dimensional Hierarchical Dynamic Space-Time Models
Ensemble Kalman Methods for High-Dimensional Hierarchical Dynamic Space-Time Models
复制标题
DOI：
10.1080/01621459.2019.1592753
复制
发表时间：
2017-04
影响因子：
3.7
通讯作者：
M. Katzfuss;Jonathan R. Stroud;C. Wikle
中科院分区：
文献类型：
--
作者：
M. Katzfuss;Jonathan R. Stroud;C. Wikle
摘要：我们提出了一类新的滤波和平滑方法，用于高维、非线性、非高斯、时空状态空间模型中的推理。主要思想是将地球物理学文献中开发的集成卡尔曼滤波器和平滑器与统计文献中的状态空间算法相结合。我们的算法适用于各种估计场景，包括在线和离线状态和参数估计。我们采用贝叶斯观点，其目标是从状态和参数的联合后验分布生成样本。我们方法的主要优点是使用集成卡尔曼方法进行降维，这允许推断高维状态向量。我们将我们的方法与现有方法进行比较，包括集成卡尔曼滤波器、粒子滤波器和粒子 MCMC。使用云运动的真实数据示例以及在许多非线性和非高斯场景下模拟的数据，我们表明我们的方法优于这些现有方法。本文的补充材料可在线获取。
Abstract We propose a new class of filtering and smoothing methods for inference in high-dimensional, nonlinear, non-Gaussian, spatio-temporal state-space models. The main idea is to combine the ensemble Kalman filter and smoother, developed in the geophysics literature, with state-space algorithms from the statistics literature. Our algorithms address a variety of estimation scenarios, including online and off-line state and parameter estimation. We take a Bayesian perspective, for which the goal is to generate samples from the joint posterior distribution of states and parameters. The key benefit of our approach is the use of ensemble Kalman methods for dimension reduction, which allows inference for high-dimensional state vectors. We compare our methods to existing ones, including ensemble Kalman filters, particle filters, and particle MCMC. Using a real data example of cloud motion and data simulated under a number of nonlinear and non-Gaussian scenarios, we show that our approaches outperform these existing methods. Supplementary materials for this article are available online.