Differentiable particle filters are an emerging class of models that combine sequential Monte Carlo techniques with the flexibility of neural networks to perform state space inference. This paper concerns the case where the system may switch between a finite set of state-space models, i.e. regimes. No prior approaches effectively learn both the individual regimes and the switching process simultaneously. In this paper, we propose the neural network based regime learning differentiable particle filter (RLPF) to address this problem. We further design a training procedure for the RLPF and other related algorithms. We demonstrate competitive performance compared to the previous state-of-the-art algorithms on a pair of numerical experiments.

翻译：可微分粒子滤波器是一类新兴的模型，它将序列蒙特卡洛技术与神经网络的灵活性相结合，用于执行状态空间推断。本文研究系统可能在有限个状态空间模型（即制度）之间切换的情况。现有方法无法同时有效学习各个制度及其切换过程。本文提出了基于神经网络的制度学习可微分粒子滤波器（RLPF）来解决这一问题。我们进一步为RLPF及其他相关算法设计了训练流程。通过两个数值实验，我们展示了与先前最先进算法相比的竞争性表现。