Efficient Bayesian Inference for a Gaussian Process Density Model

We reconsider a nonparametric density model based on Gaussian processes. By augmenting the model with latent Polya–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.