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We can use likelihood ratio (LR) statistics for comparing some sub-models with the LGBIII model in the classical way.
For comparing the candidate functions with each other - regarding their statistically significant differences - we use likelihood ratio tests.
Lastly, in step 3 for model comparison we use likelihood ratio tests (LRT) rather than AIC.
Most quartet algorithms use likelihood weights as opposed to distances.
We use likelihood methods to avoid excluding cases where no tumors were observed in specific groups.
A classical way of balancing fit and model complexity is to use likelihood ratio tests.
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The most popular way of estimating VTLN warps is to use likelihood-based estimation techniques [6, 7] in which a set of HMM models trained on a large population of speakers by placing 1 Gaussian per state is scored against warped features.
Future studies that use the present framework to jointly explain neural ensemble spiking activity and binary behavioral responses may use likelihood-based model selection methods (Burnham and Anderson, 2002) to further explore the mechanisms by which learning-related ensemble neural spiking activity gives rise to adaptive decision making in biological nervous systems.
The conceptual framework proposed here draws support from discriminative methods using likelihood ratios.
The model was compared with the LMM fitted in step 2 using likelihood ratio testing.
At CSEP testing centers, all the rate-based models are evaluated by using likelihood tests.
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