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To discriminate between any two models, the uncommon parameters play a major role.
Twice the difference in loge space of marginal likelihood between any two models is the Bayes Factor, 2loge(BF).
The difference (in loge space) of marginal likelihood between any two models is the loge of the Bayes Factor, loge(BF).
The statistical significance of differences in the survival gradient between any two models was assessed by fitting a single model to the 10 quintile-specific rates in question, with and without an interaction term, and applying the likelihood ratio test.
The reaction similarity between any two models is calculated using the following formula, (8) Similarity = A 2 A + B A + C A denotes the total number of shared reactions between the two organisms, whereas B and C represent the number of unique reactions in each model.
Similar(55)
The KL-optimality criterion has been recently proposed to discriminate between any two statistical models.
The Bayesian KL-optimality criterion is useful for discriminating between any two statistical models in the presence of prior information.
Hence, comparisons could be made between any two nested models to infer how different oligonucleotide or codon biases contributed to the predicted OSC frequency as shown in Table 1.
E max has been chosen as the third criterion to eliminate any tie between two models when both RMSE and E a are not minimum for a particular model.
For now, though, there don't seem to be any differences between the two models.
It is therefore not possible to use the comparison between Tables 3 and 4 to distinguish any further between the two models.
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