Modeling Correlated Arrival Events with Latent Semi-Markov Processes

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Wenzhao Lian, Vinayak Rao, Brian Eriksson, Lawrence Carin ;
Proceedings of the 31st International Conference on Machine Learning, PMLR 32(1):396-404, 2014.

Abstract

The analysis and characterization of correlated point process data has wide applications, ranging from biomedical research to network analysis. In this work, we model such data as generated by a latent collection of continuous-time binary semi-Markov processes, corresponding to external events appearing and disappearing. A continuous-time modeling framework is more appropriate for multichannel point process data than a binning approach requiring time discretization, and we show connections between our model and recent ideas from the discrete-time literature. We describe an efficient MCMC algorithm for posterior inference, and apply our ideas to both synthetic data and a real-world biometrics application.

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