Closed-form Inference and Prediction in Gaussian Process State-Space Models

Understand

We examine an analytic variational inference scheme for the Gaussian Process State Space Model (GPSSM) - a probabilistic model for system identification and time-series modelling.

• Our approach performs variational inference over both the system states and the transition function.
• We exploit Markov structure in the true posterior, as well as an inducing point approximation to achieve linear time complexity in the length of the time series.
• Contrary to previous approaches, no Monte Carlo sampling is required: inference is cast as a deterministic optimisation problem.