Modeling Correlated Arrival Events with Latent Semi-Markov Processes
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.
Cite
Text
Lian et al. "Modeling Correlated Arrival Events with Latent Semi-Markov Processes." International Conference on Machine Learning, 2014.Markdown
[Lian et al. "Modeling Correlated Arrival Events with Latent Semi-Markov Processes." International Conference on Machine Learning, 2014.](https://mlanthology.org/icml/2014/lian2014icml-modeling/)BibTeX
@inproceedings{lian2014icml-modeling,
title = {{Modeling Correlated Arrival Events with Latent Semi-Markov Processes}},
author = {Lian, Wenzhao and Rao, Vinayak and Eriksson, Brian and Carin, Lawrence},
booktitle = {International Conference on Machine Learning},
year = {2014},
pages = {396-404},
volume = {32},
url = {https://mlanthology.org/icml/2014/lian2014icml-modeling/}
}