Statistical Inference in Hidden Markov Models Using k-Segment Constraints
Hidden Markov models (HMMs) are one of the most widely used statistical methods for analyzing sequence data. However, the reporting of output from HMMs has largely been restricted to the presentation of the most-probable (MAP) hidden state sequence, found via the Viterbi algorithm, or the sequence o...
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| Format: | Journal article |
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Taylor and Francis
2016
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