Using logistic regressions we identify probabilities of participating in education or training depending on individual characteristics such as gender, age, education or labour market status, which indicate that age, educational attainment, but also labour market status, occupation and sector of employment influence the probability of participation in lifelong learning.
One may also focus on a lower quantile (such as 2.5 or 5%), and identify probabilities of excess incidence or relative risk at this quantile.
Experimental results show that the identified probability of images (SNR is about 2) can reach more than 98% by this method.
While this view is not completely correct (as we have seen, Boltzmann 1868 already applied probability to the total gas), it is true that (1877b) is the first occasion where Boltzmann identifies probability of a gas state with relative volume in phase space, rather than its relative time of duration.
To identify probability of a clustering pattern occurring by chance, permutation tests were performed.
A key step of identifying probability motif is the mining of frequent probability pattern.
We identify probabilities with degrees of confidence, or credences, or "partial" beliefs of suitable agents.
Participants most often identified probability and ambiguity as sources of uncertainty.
Once the model was identified, posterior probabilities of class membership were based on Bayes theory to allocate individuals to their most likely class.
To identify the probabilities of sites under positive selection in each gene, site models were implemented where ω could vary among sites [ 58, 59], which facilitated the analysis of datasets including all mammals or cetaceans only.
