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• W2170952786 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." @default.
• W2170952786 created "2016-06-24" @default.
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• W2170952786 date "2008-01-01" @default.
• W2170952786 modified "2023-10-14" @default.
• W2170952786 title "Fast Gaussian process methods for point process intensity estimation" @default.
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• W2170952786 doi "https://doi.org/10.1145/1390156.1390181" @default.
• W2170952786 hasPublicationYear "2008" @default.
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