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2007

Exploring the state sequence space for hidden Markov and semi-Markov chains

9 years 2 months ago
Exploring the state sequence space for hidden Markov and semi-Markov chains
The knowledge of the state sequences that explain a given observed sequence for a known hidden Markovian model is the basis of various methods that may be divided into three categories: (i) enumeration of state sequences, (ii) summary of the possible state sequences in state profiles, (iii) computation of a global measure of the state sequence uncertainty. Concerning the first category, the generalized Viterbi algorithm for computing the top L most probable state sequences and the forward-backward algorithm for sampling state sequences are derived for hidden semi-Markov chains and hidden hybrid models combining Markovian and semiMarkovian states. Concerning the second category, a new type of state (and state change) profiles is proposed. The Viterbi forward-backward algorithm for computing these state profiles is derived for hidden semi-Markov chains and hidden hybrid models combining Markovian and semi-Markovian states. Concerning the third category, an algorithm for computing th...
Yann Guédon
Added 13 Dec 2010
Updated 13 Dec 2010
Type Journal
Year 2007
Where CSDA
Authors Yann Guédon
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