Search Machine Learning Repository: @inproceedings{icml2014c1_lian14,
    Publisher = {JMLR Workshop and Conference Proceedings},
    Title = {Modeling Correlated Arrival Events with Latent Semi-Markov Processes},
    Url = {http://jmlr.org/proceedings/papers/v32/lian14.pdf},
    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.},
    Author = {Wenzhao Lian and Vinayak Rao and Brian Eriksson and Lawrence Carin},
    Editor = {Tony Jebara and Eric P. Xing},
    Year = {2014},
    Booktitle = {Proceedings of the 31st International Conference on Machine Learning (ICML-14)},
    Pages = {396-404}
   }