Scalable Nonparametric Bayesian Inference on Point Processes with Gaussian Processes
Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2227-2236, 2015.
In this paper we propose an efficient, scalable non-parametric Gaussian process model for inference on Poisson point processes. Our model does not resort to gridding the domain or to introducing latent thinning points. Unlike competing models that scale as O(n^3) over n data points, our model has a complexity O(nk^2) where k << n. We propose a MCMC sampler and show that the model obtained is faster, more accurate and generates less correlated samples than competing approaches on both synthetic and real-life data. Finally, we show that our model easily handles data sizes not considered thus far by alternate approaches.