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Publication Detail
Fast Gaussian process methods for point process intensity estimation
  • Publication Type:
  • Authors:
    Cunningham JP, Shenoy KV, Sahani M
  • Publication date:
  • Pagination:
    192, 199
  • Published proceedings:
    Proceedings of the 25th International Conference on Machine Learning
  • ISBN-13:
  • Status:
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. Copyright 2008 by the author(s)/owner(s).
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