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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
3
) 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.
References
[FST98] Shai Fine, Yoram Singer, and Naftali Tishby. The
hierarchical Hidden Markov Model: Analysis and applications.
Machine Learning
, 32:41, 1998.
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