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For this last step, we use the Viterbi algorithm as implemented by Simulink â"‡.
This implies that, given a specific sequence of input samples u1, u2,..., the CEC-TCQ encoder should use the Viterbi algorithm based on the path-cost function ∑ n u n - û n 2 + β E - log 2 P û n | Y n. (25).
Here, we use the Viterbi algorithm [16] for finding the most probable state sequence for a given observation in a hidden Markov model, suitably modified to make it applicable to a Markov chain, to find most probable sequence of signs.
To infer the correct states along the genome, we use the Viterbi algorithm (Viterbi, 1967).
To obtain the aligned expression, we use the Viterbi algorithm to align the time points of a patient to the most likely states of the HMM.
We can use the Viterbi algorithm to find a maximum likelihood (ML) path between two genes at distant time points in a hidden Markov model (HMM) [ 7].
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This was done using the Viterbi algorithm [54], which is a dynamic programming algorithm that allows us to determine the longest segments of time during which the f0 is approximately constant.
These probabilities are decoded using the Viterbi algorithm.
It can be determined in polynomial time using the Viterbi algorithm [21].
The stretch where they match is aligned with the audio using the Viterbi algorithm.
Each model produces a probability value by backtracking through model states using the Viterbi algorithm.
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