Skip to yearly menu bar Skip to main content


Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models

Alessandro Davide Ialongo · Mark van der Wilk · James Hensman · Carl E Rasmussen

Pacific Ballroom #215

Keywords: [ Time Series and Sequence Models ] [ Gaussian Processes ] [ Bayesian Methods ] [ Approximate Inference ]


We identify a new variational inference scheme for dynamical systems whose transition function is modelled by a Gaussian process. Inference in this setting has either employed computationally intensive MCMC methods, or relied on factorisations of the variational posterior. As we demonstrate in our experiments, the factorisation between latent system states and transition function can lead to a miscalibrated posterior and to learning unnecessarily large noise terms. We eliminate this factorisation by explicitly modelling the dependence between state trajectories and the low-rank representation of our Gaussian process posterior. Samples of the latent states can then be tractably generated by conditioning on this representation. The method we obtain gives better predictive performance and more calibrated estimates of the transition function, yet maintains the same time and space complexities as mean-field methods.

Live content is unavailable. Log in and register to view live content