Efficient Inference of Gaussian Process Modulated Renewal Processes with Application to Medical Event Data

19 Feb 2014Thomas A. Lasko

The episodic, irregular and asynchronous nature of medical data render them difficult substrates for standard machine learning algorithms. We would like to abstract away this difficulty for the class of time-stamped categorical variables (or events) by modeling them as a renewal process and inferring a probability density over continuous, longitudinal, nonparametric intensity functions modulating that process... (read more)

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