Modeling correlated arrival events with latent semi-Markov processes

Published

Conference Paper

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.

Duke Authors

Cited Authors

  • Lian, W; Rao, V; Eriksson, B; Carin, L

Published Date

  • January 1, 2014

Published In

  • 31st International Conference on Machine Learning, Icml 2014

Volume / Issue

  • 1 /

Start / End Page

  • 620 - 629

International Standard Book Number 13 (ISBN-13)

  • 9781634393973

Citation Source

  • Scopus