Related papers: Continuous-time Particle Filtering for Latent Stochastic Differential Equations

Continuous-time Particle Filtering for Latent Stochastic Differential Equations

URL: http://arxiv.org/abs/2209.00173v1
Date: Thu, 1 Sep 2022 01:05:31 GMT
Title: Continuous-time Particle Filtering for Latent Stochastic Differential Equations
Authors: Ruizhi Deng, Greg Mori, Andreas M. Lehrmann
Abstract summary: 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 techniques relying on a learned variational posterior.
Score: 37.51802583388233
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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 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 techniques relying on a learned variational posterior. Our experiments with different model families based on latent neural stochastic differential equations demonstrate superior performance of continuous-time particle filtering in inference tasks like likelihood estimation and sequential prediction for a variety of stochastic processes.

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