Going with flow: transport methods and neural networks for sequential Monte Carlo methods

Yunpeng Li (University of Surrey )

Friday 11th March, 2022 15:00-16:00 Zoom

Abstract

Sequential state estimation in non-linear and non-Gaussian state spaces has a wide range of applications in signal processing and statistics. One of the most effective non-linear filtering approaches, particle filters a.k.a. sequential Monte Carlo methods, suffer from weight degeneracy in high-dimensional filtering scenarios. A particular challenge for the deployment of particle filters is the need to specify the often nonlinear models that simulate state dynamics and their relation to measurements. This becomes non-trivial for practitioners when dealing with complex environments and big data. In the first part of the talk, I will present new filters which incorporate physics-inspired particle flow methods into an encompassing particle filter framework. The valuable theoretical guarantees concerning particle filter performance still apply, but we can exploit the attractive performance of the particle flow methods. The second part of the talk will focus on learning different components of particle filters through neural networks particularly normalizig flow, to provide flexibility to apply particle filters in large-scale real-world applications.

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