The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM

Saeedi, A., Hoffman, M. D., Johnson, M. J., & Adams, R. P. (2016). The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM. Proceedings of the 33rd International Conference on Machine Learning (ICML).

We propose the segmented iHMM (siHMM), a hierarchical infinite hidden Markov model (iHMM) that supports a simple, efficient inference scheme. The siHMM is well suited to segmentation problems, where the goal is to identify points at which a time series transitions from one relatively stable regime to a new regime. Conventional iHMMs often struggle with such problems, since they have no mechanism for distinguishing between high- and low-level dynamics. Hierarchical HMMs (HHMMs) can do better, but they require much more complex and expensive inference algorithms. The siHMM retains the simplicity and efficiency of the iHMM, but outperforms it on a variety of segmentation problems, achieving performance that matches or exceeds that of a more complicated HHMM.

  @conference{saeedi2016segmented,
  year = {2016},
  author = {Saeedi, Ardavan and Hoffman, Matthew D. and Johnson, Matthew J. and Adams, Ryan P.},
  title = {The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM},
  booktitle = {Proceedings of the 33rd International Conference on Machine Learning (ICML)},
  keywords = {ICML, time series, Bayesian nonparametrics},
  note = {arXiv:1602.06349 [stat.ML]}
}