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Particle filtering is a standard Monte-Carlo approach for a wide range ofsequential inference tasks. The key component of a particle filter is a set ofparticles with importance weights that serve as a proxy of the true posteriordistribution of some stochastic process. In this work, we propose continuouslatent particle filters, an approach that extends particle filtering to thecontinuous-time domain. We demonstrate how continuous latent particle filterscan be used as a generic plug-in replacement for inference techniques relyingon a learned variational posterior. Our experiments with different modelfamilies based on latent neural stochastic differential equations demonstratesuperior performance of continuous-time particle filtering in inference taskslike likelihood estimation and sequential prediction for a variety ofstochastic processes.

Continuous-time Particle Filtering for Latent Stochastic Differential Equations