Learning Continuous-Time Hidden Markov Models for Event Data

@inproceedings{Liu2017LearningCH,
  title={Learning Continuous-Time Hidden Markov Models for Event Data},
  author={Yu-Ying Liu and Alexander Moreno and Shuang Li and Fuxin Li and Le Song and James M. Rehg},
  booktitle={Mobile Health - Sensors, Analytic Methods, and Applications},
  year={2017}
}
The Continuous-Time Hidden Markov Model (CT-HMM) is an attractive modeling tool for mHealth data that takes the form of events occurring at irregularly-distributed continuous time points. However, the lack of an efficient parameter learning algorithm for CT-HMM has prevented its widespread use, necessitating the use of very small models or unrealistic constraints on the state transitions. In this paper, we describe recent advances in the development of efficient EM-based learning methods for CT… 
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8 Citations
Background Citations
3
Methods Citations
3

8 Citations

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