Scalable Nonparametric Bayesian Inference on Point Processes with Gaussian Processes
published: Dec. 5, 2015, recorded: October 2015, views: 1544
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
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(n3) over n data points, our model has a complexity O(nk2) 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.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !