Fast Bayesian Intensity Estimation for the Permanental Process

@InProceedings{pmlr-v70-walder17a,
  title = 	 {Fast {B}ayesian Intensity Estimation for the Permanental Process},
  author =       {Christian J. Walder and Adrian N. Bishop},
  booktitle = 	 {Proceedings of the 34th International Conference on Machine Learning},
  pages = 	 {3579--3588},
  year = 	 {2017},
  editor = 	 {Precup, Doina and Teh, Yee Whye},
  volume = 	 {70},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {06--11 Aug},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v70/walder17a/walder17a.pdf},
  url = 	 {https://proceedings.mlr.press/v70/walder17a.html},
  abstract = 	 {The Cox process is a stochastic process which generalises the Poisson process by letting the underlying intensity function itself be a stochastic process. In this paper we present a fast Bayesian inference scheme for the permanental process, a Cox process under which the square root of the intensity is a Gaussian process. In particular we exploit connections with reproducing kernel Hilbert spaces, to derive efficient approximate Bayesian inference algorithms based on the Laplace approximation to the predictive distribution and marginal likelihood. We obtain a simple algorithm which we apply to toy and real-world problems, obtaining orders of magnitude speed improvements over previous work.}
}