Sentence examples for meaningful probability from inspiring English sources

"There is a meaningful probability of a hard landing in China after 2013," Mr. Roubini told a financial conference in Singapore.

Inherent safety, safety factors, and multiple barriers have an important common feature: They all aim at protecting us not only against risks that can be assigned meaningful probability estimates, but also against dangers that cannot be probabilized, such as the possibility that some unanticipated type of event gives rise to an accident.

The implication of the violation of the KM assumptions is that the KM estimates in the presence of competing risks do not have a meaningful probability interpretation (Andersen et al. 2002); that is, the KM estimate of revision is not a valid estimate of the probability of revision assuming that the patient does not die.

However, the c′ and ϕ′ data obtained from field and/or laboratory tests for a project are usually limited and insufficient to provide a meaningful joint probability distribution of c′ and ϕ′ or quantify their cross-correlation.

Rules with three levels of complexity and applicability for management were defined in the tree models: (i) the repeated critical thresholds (predictor values across which fire characteristics change rapidly), (ii) the meaningful final probability classes and (iii) the trees themselves.

For each macrostate, the PG must be applied hundreds of times to ascertain a statistically meaningful average probability for the fluctuating distance constraints to be independent or redundant.

If, however, we knew the underlying structure of the data, then it would be possible to obtain an unbiased estimate by formulating a model directly in terms of meaningful parameters (probabilities and proportions) of the type in Table  3.

As a result of this simpler approach, the moving probabilities now have a meaningful interpretation: the probability of choosing a particular descriptor in the next iteration is equal to the average of the fraction of ants that have chosen that descriptor in their current model and the fraction of ants that have chosen it in their best model.

In order to enable meaningful comparisons between probability distributions, such distributions are embedded in a high-dimensional Reproducing Kernel Hilbert Space (RKHS) by means of characteristic kernels, which enable injective embedding of probabilities [4 7].

Overall, we were able to identify five meaningful clusters for probability of occurrence and impact: "high-impact prospects", "high-impact expectations", "high-impact eventualities", "medium-impact eventualities" and "high-impact surprises".

Generally, LR for a positive finding (LR+) of at least 2 and that for a negative finding (LR−) of at most 0.5 (i.e., 2−1) are mentioned as meaningful changes in probability [ 4, 5].