Variational Inference in Gaussian Processes via Probabilistic Point Assimilation
We introduce a novel variational approach for approximate inference in Gaussian process (GP) models. The key advantages of our approach are the ease with which different noise models can be incorporated and improved speed of convergence. We refer to the algorithm as probabilistic point assimilation (PPA). We introduce the algorithm firstly using the ‘weight space’ view and then through its Gaussian process formulation. We illustrate the approach on several benchmark data sets.