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User: terence-l-j-harris

The Past Does Matter: Correlation of Subsequent States in Trajectory Predictions of Gaussian Process Models. (arXiv:2211.11103v2 [stat.ML] UPDATED)

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Computing the distribution of trajectories from a Gaussian Process model of a
dynamical system is an important challenge in utilizing such models. Motivated
by the computational cost of sampling-based approaches, we consider
approximations of the model's output and trajectory distribution. We show that
previous work on uncertainty propagation, focussed on discrete state-space
models, incorrectly included an independence assumption between subsequent
states of the predicted trajectories. Expanding these ideas to continuous
ordinary differential equation models, we illustrate the implications of this
assumption and propose a novel piecewise linear approximation of Gaussian
Processes to mitigate them.

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