Conjoint Modeling of Temporal Dependencies in Event Streams

Many real world applications depend on modeling the temporal dynamics of streams of diverse events, many of which are rare. We introduce a novel model class, Conjoint

Piecewise-Constant Conditional Intensity Models, and a learning algorithm that together yield a data-driven approach to parameter sharing with the aim of better modeling such event streams. We empirically demonstrate that our approach yields more accurate models of two real world data sets: search query logs and data center system logs.

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In  UAI Bayesian Modelling Applications Workshop

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TypeInproceedings

Previous Versions

Asela Gunawardana, Christopher Meek, and Puyang Xu. A Model for Temporal Dependencies in Event Streams, Neural Information Processing Systems Foundation, December 2011.

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