Fast Gaussian process methods for point process intensity estimation

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.

• Type: Conference
• Year: 2008
• Where: ICML
• Authors: John P. Cunningham, Krishna V. Shenoy, Maneesh Sahani
• Keywords: ICML 2008, Intensity Function, Machine Learning, Problem Specific Methods, Underlying Intensity Functions