2014 The analysis 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.
|Original language||English (US)|
|Title of host publication||31st International Conference on Machine Learning, ICML 2014|
|Publisher||International Machine Learning Society (IMLS)firstname.lastname@example.org|
|Number of pages||10|
|State||Published - Jan 1 2014|