Sentence examples for ratio of probabilities from inspiring English sources
Fe3+/ΣFe = ΣAFe3+/ ΣAFe2+. + k∙ΣAFe3+), where k is the ratio of probabilities of Mössbauer transition of octahedral complexes Fe3+ and Fe2+, k is taken equal to 1.232,33.
If we assume a low p, say p = 10−6, and an unbiased r (i.e., that of a well-mixed population, r = 0.5), then we can compare the ratio of probabilities for a range of values of c and m (see Fig. 1B).
For any two alternatives that are in the same nest, the ratio of probabilities is independent of the attributes or existence of all other alternatives.
If the ratio of probabilities is less than one, i.e., ( mathbb{p}<1 ), the nodes are considered not to be normal than to selfish.
For any two alternatives in different nests, the ratio of probabilities can depend on the attributes of other alternatives in the two nests.
Case 1. (likelihood ratio of probabilities is approximately one, leading to a weak (small) classification confidence if a confidence parameterized classifier, like an SVM, is referred to in place of the simple ratio) (25).
Meanwhile, the ratio of probability of the arrival for large cars, medium cars and small cars is 1 2 6.
Odds = exp(4.048 − 0.042 × Ht); where, Ht is maternal height in cm, odds represents a ratio of probability of cesarean section to probability of vaginal delivery.
The LLS was calculated based on the prior odds (OPrior), describing the ratio of probability of functional linkage and its negation without evidence, and the posterior odds (OPosterior), describing the ratio of probability of functional linkage and its negation under the given evidence.
Based on these approximations, we derived the prior odds (Additional file 1A), i.e. the ratio of probability of functional association and its negation without evidence, and the posterior odds, i.e. the ratio of probability of functional association and its negation given the evidence, for each dataset (Additional file 1B) and subsequently determined the LLS.
Binary logistic regression was used (after meeting the assumptions) to find the influence of each statistically significant predictor variable in terms of the "odds ratio of probability".
