Continuous-time Particle Filtering for Latent Stochastic Differential Equations
Abstract: 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 the adoption of continuous-time particle filtering for different types of inference tasks in neural latent stochastic differential equations. We demonstrate how such particle filters can be used as a generic plug-in replacement for inference techniques relying on a learned variational posterior. Our experiments with different model families based on neural latent stochastic differential equations demonstrate superior performance of continuous-time particle filtering in inference tasks like likelihood estimation and sequential prediction, both in synthetic and real-world scenarios.
Submission Length: Regular submission (no more than 12 pages of main content)
Changes Since Last Submission: N/A
Assigned Action Editor: ~George_Papamakarios1
Submission Number: 2519
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