The phrase "average log likelihood" is correct and usable in written English.
It is typically used in statistical contexts, particularly in relation to model evaluation and comparison.
Example: "The average log likelihood of the model indicates how well it fits the data."
Alternatives: "mean log likelihood" or "typical log likelihood".
Figure 4 shows the average log likelihood (Negative) value of 0.653 for trees optimal N = 171 in our model.
Comparing the average log likelihood of each phoneme for adult and older speech is likely to give a good indication of the mismatch between the features.
The column similarity metrics that have been used include Pearson's correlation coefficient (PCC), p-value of Chi-square (pCS), average Kullback-Leibler (KL), Sum of squared distances (SSD), and average log likelihood ratio (ALLR), etc. [6], [9], [11] [15].
Structure analyses indicated the highest average log likelihood occurred at K = 26 (−11745.70) (Fig. 4).
However, it has been extended as the average log likelihood ratio (ALLR) [ 41] for this task.
If the average log likelihood ratio (ALLR) that the candidate gene adds is above the predefined threshold T, the gene is appended to the module.
Next we focus on the M-step: find a new Q that maximizes the averaged log likelihood function (4) where q is the posterior distribution of the variables at the internal nodes conditioned on observed data and the previously estimated parameters.
In the end, the averaged log likelihood function in Equation (1) can be written as (6) where E[ T k)] is the expected duration of state k summed over all branches of the tree, and similarly E[ N k, l)] is the expected total number of transitions from k to l.
In statistical terms, r n and r are called the likelihood ratio and the asymptotic average log-likelihood ratio, respectively [1].
In this paper, the notion of asymptotic average log-likelihood ratio, as a measure of the difference between the sequence of random variables and Markov chains, is introduced, and by constructing a nonnegative martingale, the strong deviation theorem for discrete-time and continuous-state nonhomogeneous Markov chains is established.
Table 4 shows our optimal model, where area under the ROC curve shows a measure of overall model performance of 50%% and an average Log-Likelihood of 65%% to emphasize the probability interpretation of the model predictions for the target variable STRESS (S).
