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.