Ankur P. Parikh, Asela Gunawardana, and Christopher Meek
18 August 2012
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.
In UAI Bayesian Modelling Applications Workshop
Asela Gunawardana, Christopher Meek, and Puyang Xu. A Model for Temporal Dependencies in Event Streams, Neural Information Processing Systems Foundation, December 2011.