Regime Learning for Differentiable Particle Filters

arXiv:2405.04865v1 Announce Type: new
Abstract: 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 …

abstract arxiv case class concerns cs.lg differentiable eess.sp filters flexibility inference networks neural networks paper particle prior set space state type