论文标题
合奏运输平滑。第二部分：非线性更新
Ensemble transport smoothing. Part II: Nonlinear updates
论文作者
论文摘要
平滑是一种针对状态空间模型的贝叶斯推断的一种专业形式，它表征了一系列相关的观测序列。 Ramgraber等。 （2023）提出了一个用于基于运输的集合平滑的一般框架，其中包括线性的卡尔曼型Smoothers作为特殊情况。在这里，我们建立在这个基础的基础上，以实现并证明非线性的后退合奏运输氛围。我们讨论相关运输图的参数化和正则化，然后检查这些smoOthort在表现出非高斯行为的非线性和混乱动力学系统中的性能。在这些环境中，我们的非线性传输Smoothorts的估计误差比常规线性Smoothers和最新的迭代合奏Kalman Smoothorts较低，用于可比的模型评估。
Smoothing is a specialized form of Bayesian inference for state-space models that characterizes the posterior distribution of a collection of states given an associated sequence of observations. Ramgraber et al. (2023) proposes a general framework for transport-based ensemble smoothing, which includes linear Kalman-type smoothers as special cases. Here, we build on this foundation to realize and demonstrate nonlinear backward ensemble transport smoothers. We discuss parameterization and regularization of the associated transport maps, and then examine the performance of these smoothers for nonlinear and chaotic dynamical systems that exhibit non-Gaussian behavior. In these settings, our nonlinear transport smoothers yield lower estimation error than conventional linear smoothers and state-of-the-art iterative ensemble Kalman smoothers, for comparable numbers of model evaluations.
