  Latent Gaussian models and boosting are widely used techniques in statistics
and machine learning. Tree-boosting shows excellent prediction accuracy on many
data sets, but potential drawbacks are that it assumes conditional independence
of samples, produces discontinuous predictions for, e.g., spatial data, and it
can have difficulty with high-cardinality categorical variables. Latent
Gaussian models, such as Gaussian process and grouped random effects models,
are flexible prior models which explicitly model dependence among samples and
which allow for efficient learning of predictor functions and for making
probabilistic predictions. However, existing latent Gaussian models usually
assume either a zero or a linear prior mean function which can be an
unrealistic assumption. This article introduces a novel approach that combines
boosting and latent Gaussian models to remedy the above-mentioned drawbacks and
to leverage the advantages of both techniques. We obtain increased prediction
accuracy compared to existing approaches in both simulated and real-world data
experiments.
