Neutrosophic Binomial Distribution: The neutrosophic binomial random variable
'x' is then defined as the number of success when we perform the experiment n [greater than or equal to] 1 times.
It would then not be appropriate to test the fit of the model by using a hypothesis test that assumes the number of dry days in 1999-2008 is a binomial random variable
with distribution Bi(3653, [p.
In this model, the calling frequency corresponding to sample i (a particular day and hour) is a binomial random variable
with sample size 12, and probability [P.
n] is distributed like a binomial random variable
on n independent trials with rate of success q in each.
Let X be a binomial random variable
with parameters n and p, where p denotes the defect level of process, and let [?
The binomial random variable
r represents the number of successes in n trials of the experiment, and we know from an earlier chapter that the mean and variance of the binomial are
For this model, the number of links in the predation matrix is a binomial random variable
with n(n - 1)/2 trials and success probability y/n, so that the expected number of links is [Gamma](n - 1)/2 (i.
All three algorithms are based on the property that the binomial random variable
is the sum of n Bernoulli trials, with the probability of success in each trial being p.
Here and in what follows Bin(m) denotes a binomial random variable
with parameters m and p = 1/2.
Given that the true parasitism rate is P, X is assumed to be a binomial random variable
with mean N[multiplied by]P (the observed parasitism rate is [Mathematical Expression Omitted]).
Simulation of Bernoulli and Binomial random variables
2008, "Ordering comparison of negative binomial random variables
with their mixtures", Statistics and Probability Letters.