Sentence examples for binomial distribution to define from inspiring English sources

Using our I2D database [81], [82] we examined known human protein-protein interactions for a binomial distribution to define such hubs, and failed to find such a distribution, hence we are unable to further study any such relationship.

Frequency distribution was compared to binomial distribution to evidence the phase separation and atom clustering.

We have devoted two lines in Table 9 to the binomial distribution to illustrate its inaccuracy.

It uses a negative binomial distribution to model total variation.

We used normal approximation of binomial distribution to construct confidence intervals (CI) for each indicator.

Finally, we used a negative binomial distribution to estimate the statistical significance of the peaks.

Thus, we developed a model that uses a negative-binomial distribution to approximate an overdispersed Poisson distribution.

The probability mass function for the binomial distribution is defined to be B k ; n, p = n k p k 1 − p n − k, where n k = n ! k ! n − k !, n is the number of trials, k is the number of successes, and p is the probability of success.

The probability mass function of binomial distribution is defined in the following way: (18).

Comparisons are made with the generalized negative binomial distribution (GNBD) defined by Jain and Consul ([1971]) and the exponentiated-exponential geometric distribution (EEGD) defined by Alzaatreh et al. ([2012b]).

The KBIN procedure follows from basic probability concepts, to control the probability that the number of false positives V is not larger than k to be α, that is, where V is the number of false positives, assumed to follow a Binomial distribution, with k defined by the user.