Linear Time Inference in Hierarchical HMMs (2001) [54 citations — 5 self]
http://www-2.cs.cmu.edu/Groups/NIPS/NIPS2001/paper
http://http.cs.berkeley.edu/~murphyk/Papers/hhmm.p
http://books.nips.cc/papers/files/nips14/AA64.pdf
http://www.cs.ubc.ca/~murphyk/Papers/hhmm_nips01.p
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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.
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