A Bayesian Adaptive Ensemble Kalman Filter for Sequential State and Parameter Estimation
A Bayesian Adaptive Ensemble Kalman Filter for Sequential State and Parameter Estimation
复制标题
DOI：
10.1175/mwr-d-16-0427.1
复制
发表时间：
2018-01-01
影响因子：
3.2
通讯作者：
Wikle, Christopher K.
中科院分区：
文献类型：
--
作者：
Stroud, Jonathan R.;Katzfuss, Matthias;Wikle, Christopher K.
本文提出了一种新的集合卡尔曼滤波器的序列状态和参数估计方法。该方法是完全贝叶斯和传播的状态和参数随时间的联合后验分布。为了实现该方法，作者认为三个代表性的边缘后验分布的参数：基于网格的方法，高斯近似，和一个连续的重要性采样（SIR）的方法与内核reservation。与现有的在线参数估计算法相比，新方法明确地考虑了参数的不确定性，并提供了一种正式的方式来联合收割机的参数信息，从数据在不同的时间段。该方法的说明和现有的方法相比，使用模拟和真实的数据。
This paper proposes new methodology for sequential state and parameter estimation within the ensemble Kalman filter. The method is fully Bayesian and propagates the joint posterior distribution of states and parameters over time. To implement the method, the authors consider three representations of the marginal posterior distribution of the parameters: a grid-based approach, a Gaussian approximation, and a sequential importance sampling (SIR) approach with kernel resampling. In contrast to existing online parameter estimation algorithms, the new method explicitly accounts for parameter uncertainty and provides a formal way to combine information about the parameters from data at different time periods. The method is illustrated and compared to existing approaches using simulated and real data.