Aim of this package
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 (
in general using the exported API is sufficient.
The main API is:
posterior(approximation, lfx::LatentFiniteGP, ys)to obtain the posterior approximation to
lfxconditioned on the observations
approx_lml(approximation, lfx::LatentFiniteGP, ys)which returns the marginal likelihood approximation that can be used for hyperparameter optimisation.
Currently implemented approximations:
NOTE: requires optimisation of the variational distribution even for fixed hyperparameters.