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Inference in non-linear dynamical systems -- a machine learning perspective

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If you have a question about this talk, please contact Dr Jason Z JIANG.

Inference in discrete-time non-linear dynamical systems is often done using the Extended Kalman Filtering and Smoothing (EKF) algorithm, which provides a Gaussian approximation to the posterior based on local linearisation of the dynamics. In challenging problems, when the non-linearities are significant and the signal to noise ratio is poor, the EKF performs poorly. In this talk we will discuss an alternative algorithm developed in the machine learning community which is based message passing in Factor Graphs and the Expectation Propagation (EP) approximation. We will show this method provides a consistent and accurate Gaussian approximation to the posterior enabling learning using Expectation Maximisation (EM) even in cases when the EKF fails.

This talk is part of the CUED Control Group Seminars series.

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