Linear Time Inference in Hierarchical HMMs (2001)
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| Venue: | In Proceedings of Neural Information Processing Systems |
| Citations: | 71 - 5 self |
BibTeX
@INPROCEEDINGS{Murphy01lineartime,
author = {Kevin P. Murphy and Mark A. Paskin},
title = {Linear Time Inference in Hierarchical HMMs},
booktitle = {In Proceedings of Neural Information Processing Systems},
year = {2001}
}
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Abstract
The hierarchical hidden Markov model (HHMM) is a generalization of the hidden Markov model (HMM) that models sequences with structure at many length/time scales [FST98]. Unfortunately, the original inference algorithm is rather complicated, and takes O(T ) time, where T is the length of the sequence, making it impractical for many domains. In this paper, we show how HHMMs are a special kind of dynamic Bayesian network (DBN), and thereby derive a much simpler inference algorithm, which only takes O(T ) time. Furthermore, by drawing the connection between HHMMs and DBNs, we enable the application of many standard approximation techniques to further speed up inference.
