Sentence examples for cumulative density distribution from inspiring English sources

Third: using the Cumulative Density distribution function fitted to present data to transform projected population and (1-HDI) values into a uniform probability interval [0, 1] (see Supplementary Fig. 17).

Fig. 5 Cumulative density distribution of voltage instability for correlation κ = 0.162 Fig. 6 Cumulative density distribution of voltage instability for correlation κ = 0.257.

Fig. 3 Cumulative density distribution of voltage instability with critical power.

where P i is the probability of events to use each energy choice, θ (X′θ) is the standard normal cumulative density distribution function, and X and θ are vectors of explanatory variables and their coefficients respectively [42].

Second, we estimated the cumulative density distribution of branch length [ 56].

This result was inconsistent with the result recovered using the Venditti's method [ 56]; the observed curve of cumulative density distribution of branch length corresponded to variable-rates cumulative distribution function.

This heuristic approach is common in optimisation techniques, but is essential in determining required probability and cumulative density distributions.

For correlation coefficient κ = 0.280 and κ = 0.794, the cumulative density distributions of voltage instability are expressed in Figs. 8 and 9, respectively.

We end this paper with a short conclusion in Section 4. Let {X1,…,X m } and {Y1,…,Y n } be two independent samples from the continuous cumulative density distributions F x) and G(x−θ), respectively.

The cumulative probability density distribution represented by Eq. (16) is referred to as a fragility function.

Using the set of these 100 highest-synergy scores, we obtained the maximum likelihood estimates of the location parameter and the scale parameter of the Gumbel (type-I extreme value) distribution, resulting in a cumulative density function F. The Gumbel distribution [ 43] is the limiting distribution of the maximum of a large number of random observations from the same arbitrary distribution.