Sentence examples for a common probability distribution from inspiring English sources

Moreover, baseline models may differ from the to-be-compared networks, e.g., between subjects or conditions, which certainly complicates the estimation of a common probability distribution.

It is important to note that, unlike traditional significance hypothesis testing methods, our new methodology does not assume homogeneity of the sample under a common probability distribution.

The conditional density in (3) differs from the density of duplication times derived by Hohna [ 44], in which the duplication events are treated as a random sample from a common probability distribution.

We do not make this assumption but instead note that it is reasonable to assume that the variation of the copy number can be modelled by a common probability distribution across all cells.

Let H = −12 Δ + V on l2(Z), where V x), xϵZ are i.i.d.r.v.'s with common probability distribution μ.

random variables, f is their common probability distribution function (pdf) with variance σ f 2. This implies that the overall power at the receiver is equal to m σ f 2. If m n S N R ≤ η, we will have t r ( E 1 m A X X * A * ≤ n η σ f 2. (18).

The function f(x)=e-x², where e=2.71828... is Euler's number, describes the most common probability distribution seen in the real world, governing everything from SAT scores to locations of darts thrown at a target.

Because the cumulative distribution functions for many common probability distributions are sigmoidal, these curves are typically the result of a probabilistic mechanism.

To put the highest density contour method in context, we compare it to the established inverse first-order reliability method (IFORM) and show that for common probability distributions the two methods yield similarly shaped contours.

The conditional mean, variance and skewness of both x and y = ln (x) are derived, and combined with numerous common probability distributions including the lognormal, generalized extreme value and log Pearson type III models, resulting in a very simple and general approach to NFFA.

Nodes access the medium based on a common probability.