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	Department of Engineering Computational and Biological Learning Lab

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MP Deisenroth, MF Huber, UD Hanebeck
Analytic Moment-based Gaussian Process Filtering
in Proceedings of the 26th International Conference on Machine Learning (ICML), June 2009, Montreal, Canada.

We propose an analytic moment-based filter for nonlinear stochastic dynamic systems modeled by Gaussian processes. Exact expressions for the expected value and the covariance matrix are provided for both the prediction step and the filter step, where an additional Gaussian assumption is exploited in the latter case. Our filter does not require further approximations. In particular, it avoids finite-sample approximations. We compare the filter to a variety of Gaussian filters, that is, the EKF, the UKF, and the recent GP-UKF proposed by Ko et al. (2007).

MPI for Biological Cybernetics, Tübingen, Germany