论文标题
集合卡尔曼变异目标：非线性潜在轨迹推断，具有变异推理和集合卡尔曼滤波器的混合体
Ensemble Kalman Variational Objectives: Nonlinear Latent Trajectory Inference with A Hybrid of Variational Inference and Ensemble Kalman Filter
论文作者
论文摘要
贝叶斯非线性过滤结合使用变异推理（VI）可为潜在的时间序列建模产生最新结果。最近的一系列工作集中在顺序的蒙特卡洛（SMC）及其变体，例如向后滤波向后模拟（FFBSI）。尽管这些研究成功了，但粒子退化和偏置梯度估计量仍然存在严重问题。在本文中，我们提出了ENSEMEL KALMAN变量目标（ENKO），即VI的混合方法和集合Kalman滤波器（ENKF），以推断状态空间模型（SSMS）。我们提出的方法可以有效地识别潜在动力学，因为其粒子多样性和无偏梯度估计器。我们证明，在三个基准非线性系统识别任务的预测能力和粒子效率方面，我们的ENKO优于基于SMC的方法。
Variational inference (VI) combined with Bayesian nonlinear filtering produces state-of-the-art results for latent time-series modeling. A body of recent work has focused on sequential Monte Carlo (SMC) and its variants, e.g., forward filtering backward simulation (FFBSi). Although these studies have succeeded, serious problems remain in particle degeneracy and biased gradient estimators. In this paper, we propose Ensemble Kalman Variational Objective (EnKO), a hybrid method of VI and the ensemble Kalman filter (EnKF), to infer state space models (SSMs). Our proposed method can efficiently identify latent dynamics because of its particle diversity and unbiased gradient estimators. We demonstrate that our EnKO outperforms SMC-based methods in terms of predictive ability and particle efficiency for three benchmark nonlinear system identification tasks.
