Imputing Missing Events in Continuous-Time Event Streams

international conference on machine learning, 2019.

Cited by: 4|Bibtex|Views3|Links
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Abstract:

Events in the world may be caused by other, unobserved events. We consider sequences of events in continuous time. Given a probability model of complete sequences, we propose particle smoothing---a form of sequential importance sampling---to impute the missing events in an incomplete sequence. We develop a trainable family of proposal d...More

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