Provide various algorithms for approximate inference in latent Gaussian process models, currently focussing on non-conjugate (non-Gaussian) likelihoods and sparse approximations.

Each approximation lives in its own submodule (<Approximation>Module), though in general using the exported API is sufficient.

The main API is:

• posterior(approximation, lfx::LatentFiniteGP, ys) to obtain the posterior approximation to lfx conditioned on the observations ys.

• approx_lml(approximation, lfx::LatentFiniteGP, ys) which returns the marginal likelihood approximation that can be used for hyperparameter optimisation.

Currently implemented approximations:

• LaplaceApproximation

• SparseVariationalApproximation

  NOTE: requires optimisation of the variational distribution even for fixed hyperparameters.