  We reconsider a nonparametric density model based on Gaussian processes. By
augmenting the model with latent P\'olya--Gamma random variables and a latent
marked Poisson process we obtain a new likelihood which is conjugate to the
model's Gaussian process prior. The augmented posterior allows for efficient
inference by Gibbs sampling and an approximate variational mean field approach.
For the latter we utilise sparse GP approximations to tackle the infinite
dimensionality of the problem. The performance of both algorithms and
comparisons with other density estimators are demonstrated on artificial and
real datasets with up to several thousand data points.
