论文标题
在非线性状态空间模型中推断推理的平行方形统计线性回归
Parallel square-root statistical linear regression for inference in nonlinear state space models
论文作者
论文摘要
在本文中，我们使用统计线性回归和迭代的统计后验后线性化范式介绍了一般非线性非高斯状态空间模型中的状态和参数估计的平行方法。我们还以方形形式重新重新制定了提出的方法，从而提高了数值稳定性，同时保留了并行化功能。然后，我们利用方法的固定点结构来在对数时间中相对于观测值的对数时间进行基于似然的参数估计。最后，我们通过在图形处理单元（GPU）上运行的数值实验来证明该方法的实际性能。
In this article, we introduce parallel-in-time methods for state and parameter estimation in general nonlinear non-Gaussian state-space models using the statistical linear regression and the iterated statistical posterior linearization paradigms. We also reformulate the proposed methods in a square-root form, resulting in improved numerical stability while preserving the parallelization capabilities. We then leverage the fixed-point structure of our methods to perform likelihood-based parameter estimation in logarithmic time with respect to the number of observations. Finally, we demonstrate the practical performance of the methodology with numerical experiments run on a graphics processing unit (GPU).
