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  1. ; (, American Institute of Aeronautics and Astronautics)
    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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