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A simple algorithm based on the maximum likelihood is proposed.

Since the two trees were highly similar, only maximum likelihood is shown here.

Estimation of model parameters via the method of maximum likelihood is presented in section 6.

The speaker with the maximum likelihood is determined as the target speaker.

The method of maximum likelihood is used for estimating the coefficients of the estimators.

Estimation of the model parameters by maximum likelihood is investigated in Section 6.

In the second case, reconciliation using maximum likelihood is formally identical to the conventional least-squares solution.

If random coefficients are involved, maximum likelihood is not feasible and alternative estimation methods have to be employed.

The principle of maximum likelihood is employed to learn the parameters of individual layer in the HDLA model.

After optimization of the patterns, maximum likelihood is adopted to redesign the intracranial area into two clusters.

The maximum likelihood is l = ∑ d i ∈ D ∑ w j ∈ W n ( d i, w j ) log P ( d i, w j ).

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