  Existing methods for structure discovery in time series data construct
interpretable, compositional kernels for Gaussian process regression models.
While the learned Gaussian process model provides posterior mean and variance
estimates, typically the structure is learned via a greedy optimization
procedure. This restricts the space of possible solutions and leads to
over-confident uncertainty estimates. We introduce a fully Bayesian approach,
inferring a full posterior over structures, which more reliably captures the
uncertainty of the model.
