Fast Gaussian process methods for point process intensity estimation

Authors:
John P. Cunningham;Krishna V. Shenoy;Maneesh Sahani
Affiliations:
Stanford University, Stanford, CA;Stanford University, Stanford, CA;Gatsby Computational Neuroscience Unit, UCL London, UK
Venue:
Proceedings of the 25th international conference on Machine learning
Year:
2008

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Abstract

Point processes are difficult to analyze because they provide only a sparse and noisy observation of the intensity function driving the process. Gaussian Processes offer an attractive framework within which to infer underlying intensity functions. The result of this inference is a continuous function defined across time that is typically more amenable to analytical efforts. However, a naive implementation will become computationally infeasible in any problem of reasonable size, both in memory and run time requirements. We demonstrate problem specific methods for a class of renewal processes that eliminate the memory burden and reduce the solve time by orders of magnitude.