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We propose an online variational inference framework for joint parameter-state estimation in nonlinear systems. This approach provides a probabilistic estimate of both parameters and states, and does so without relying on a mean-field assumption of independence of the two. The proposed method leverages a factorized form of the target posterior distribution to enable an effective pairing of variational inference for the marginal posterior of parameters with conditional Gaussian filtering for the conditional posterior of the states. This factorization is retrained at every time-step via formulation that combines variational inference and regression. The effectiveness of the framework is demonstrated through applications to two example systems, where it outperforms both the joint Unscented Kalman Filter and Bootstrap Particle Filter parameter-state augmentation in numerical experiments.
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Streaming Variational Monte Carlo
Nonlinear state-space models are powerful tools to describe dynamical structures in complex time series. In a streaming setting where data are processed one sample at a time, simultaneous inference of the state and its nonlinear dynamics has posed significant challenges in practice. We develop a novel online learning framework, leveraging variational inference and sequential Monte Carlo, which enables flexible and accurate Bayesian joint filtering. Our method provides an approximation of the filtering posterior which can be made arbitrarily close to the true filtering distribution for a wide class of dynamics models and observation models. Specifically, the proposed framework can efficiently approximate a posterior over the dynamics using sparse Gaussian processes, allowing for an interpretable model of the latent dynamics. Constant time complexity per sample makes our approach amenable to online learning scenarios and suitable for real-time applications.
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- Award ID(s):
- 1734910
- PAR ID:
- 10344229
- Date Published:
- Journal Name:
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- ISSN:
- 0162-8828
- Page Range / eLocation ID:
- 1 to 1
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/TPAMI.2022.3153225
