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

@article{Lasko2014EfficientIO,
  title={Efficient Inference of Gaussian Process Modulated Renewal Processes with Application to Medical Event Data},
  author={Thomas A. Lasko},
  journal={Uncertainty in artificial intelligence : proceedings of the ... conference. Conference on Uncertainty in Artificial Intelligence},
  year={2014},
  volume={2014},
  pages={469-476}
}
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 non-parametric longitudinal intensity functions that modulate the process. Several methods exist for inferring such a density over intensity functions, but either their… CONTINUE READING
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