Scalable Joint Models for Reliable Uncertainty-Aware Event Prediction

16 Aug 2017 Hossein Soleimani James Hensman Suchi Saria

Missing data and noisy observations pose significant challenges for reliably predicting events from irregularly sampled multivariate time series (longitudinal) data. Imputation methods, which are typically used for completing the data prior to event prediction, lack a principled mechanism to account for the uncertainty due to missingness... (read more)

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