Variational approximations

[chris-nemeth]

In the case of non-Gaussian likelihoods, we're using the mcmc function to infer the model parameters and latent function, which we can think of as our posterior. Alternatively, we could use variational inference to get an approximation to the posterior. This is done using optimisation rather than sampling so should be considerably faster.

We could implement the ADVI algorithm, which in our case is even simpler as our latent function and parameters are already defined on the unconstrained real space. Therefore, we'd essentially be using optimisation find a Normal approximation to our posterior.