Continuous-time Particle Filtering for Latent Stochastic Differential Equations. (arXiv:2209.00173v1 [cs.LG])

Particle filtering is a standard Monte-Carlo approach for a wide range of
sequential inference tasks. The key component of a particle filter is a set of
particles with importance weights that serve as a proxy of the true posterior
distribution of some stochastic process. In this work, we propose continuous
latent particle filters, an approach that extends particle filtering to the
continuous-time domain. We demonstrate how continuous latent particle filters
can be used as a generic plug-in replacement for inference …

arxiv continuous filtering stochastic time