Sentence examples for distribution success from inspiring English sources

In fact, pure-play distribution success leads to higher and higher losses due to sobering industry economics these pure-plays can't stomach, but the behemoths can due to their multi-pronged business models.

The event was a mix of speakers showing examples of (predominantly) distribution success stories and more intimate lunch discussions.

Based on the total number of clonal observations made (n = 96), the probability that a common progenitor for the left and right ventricles does not exist, assuming a binomial distribution (success, or 1, = the progenitor is observed; failure, or 0 = the progenitor is not observed), can be calculated using Jeffreys interval.

Accordingly, the sCMP class further captures the special case distributions of the CMP model: a geometric distribution with success probability, p=1−λ, when m=1, ν=0, and λ<1; and a Bernoulli distribution with success probability (p_{ast = } frac {lambda }{1+lambda }) when m=1 and ν→∞.

(In other words, the distribution of success is skewed towards both tails).

Even in this age of inequality, there's nothing as unequal as the distribution of success in Silicon Valley.

"Microsoft," the government declared, "has repeatedly, and in a variety of ways, used the monopoly power of its operating systems to prevent the development, distribution and success of middleware products".

The geometric distribution with success probability p is a special case of negative binomial (n,p) where n=1.

Meanwhile, for ν=0 and λ<1 (i.e. the geometric distribution with success probability 1−λ), we confirm that the ratio between probabilities of two consecutive values is constant, equaling (frac {1}{lambda }>1).

The distribution of success and failure to social groups is supported by lay theories about the characteristics of social groups and the causes of their outcomes, as well as by beliefs about entitlement of groups to succeed or fail.

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.