The phrase "applying the likelihood" is correct and usable in written English.
It can be used in contexts related to statistics, probability, or decision-making where one is considering the likelihood of certain outcomes or events.
Example: "In our analysis, we are applying the likelihood of various scenarios to determine the best course of action."
Alternatives: "utilizing the probability" or "considering the chances".
Check to see if the addition of a gaussian line to the null power-law model is significant, by applying the likelihood ratio test to compare a power-law (p1) with a power-law plus a line (p1+g1).
The present study focuses on practical issues when applying the Likelihood Ratio statistic according to Andersen (1973) for testing the binary Rasch Model.
Applying the likelihood function (Eq. 4) and the joint prior distribution given in Eq. 8 to the calculation of the joint posterior distribution (Eq. 3) gives the following expression, after simplification (cancelling of factors present both at the numerator and the denominator): Open image in new window (11 where Open image in new window.
Applying the likelihood ratio test by calculating un-weighted models yielded that our count data are over-dispersed.
Model improvement was tested by applying the likelihood ratio test where p < 0.05 indicated a model improvement.
The European brown hare time series were tested for non-linearity in X (t) vs. X (t-1) by applying the likelihood ratio test [ 50].
In particular, the in-degree distribution obeys to a power-law with the exponent α=5.12 as a result in applying the likelihood-based method presented in [22], while the out-degree and degree distributions exhibit a particular behavior due to presence of more nodes with high degree than expected in a heavy-tail distribution.
We applied the likelihood ratio test [13] for all hypothesis testing.
Next, we apply the likelihood ratio (LR) statistics to ascertain the direction of Granger causality between the variables of interest.
We applied the likelihood ratio test (cf. Gould et al. 2006): {sum2=2{displaystyle {sum}_1^c}{n}_i log left({n}_i/widehat{mu_l}right kern2em mathrm{with}kern0.5em i = 1(1)mathrm{c} (1).
Then Model 4 is a special case of the model in which the outcome y comes from a binomial distribution with success rate p j, where j = 1, 2, …, J. Thus evaluating the model fit reduces to the comparison between two nested models, hence we can apply the likelihood ratio test.
