A Bayesian approach to on-line learning
On-line learning in neural networks
Learning nonlinear dynamical systems using an EM algorithm
Proceedings of the 1998 conference on Advances in neural information processing systems II
Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches
Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Tractable inference for complex stochastic processes
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Learning GP-BayesFilters via Gaussian process latent variable models
Autonomous Robots
Multimodal nonlinear filtering using Gauss-Hermite quadrature
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
A Bayesian nonparametric approach to modeling motion patterns
Autonomous Robots
Dynamic GP models: an overview and recent developments
ASM'12 Proceedings of the 6th international conference on Applied Mathematics, Simulation, Modelling
Probabilistic movement modeling for intention inference in human-robot interaction
International Journal of Robotics Research
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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).