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We present two different methods for scoring a DAG: one based on parsimony and one based on likelihood.
Several criteria were used, one based on likelihood maximisation (MLE) and two based on discriminative criteria (FD and FDW).
Variables will be eliminated one by one from the model based on likelihood ratio tests.
The work of Pollock et al.[ 7] presented one such method, based on likelihood ratio tests.
c Multi-bits based on likelihood ratio [6].
Scan statistics are based on likelihood ratio tests.
For each trait, the most appropriate model was chosen based on likelihood ratio tests (LRT).
Our method employs an efficient architecture and a low complexity training algorithm based on likelihood maximization.
The classical SDE parameter estimation methods are largely based on likelihood estimation, which is computationally expensive.
Finally we opted for TM with model selection through AIC, a method based on likelihood and model complexity.
The most suitable model amongst all six models was determined based on likelihood ratio tests for each trait.
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