Fast Gaussian process methods for point process intensity estimation.
John P Cunningham, Krishna V Shenoy and Maneesh Sahani
ICML ’08: Proceedings of the 25th international conference on Machine learning
, Helsinki, Finland
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