binomial distribution


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Related to binomial distribution: Poisson distribution, normal distribution

binomial distribution

n.
The frequency distribution of the probability of a specified number of successes in an arbitrary number of repeated independent Bernoulli trials. Also called Bernoulli distribution.

binomial distribution

n
(Statistics) a statistical distribution giving the probability of obtaining a specified number of successes in a specified number of independent trials of an experiment with a constant probability of success in each. Symbol: Bi (n, p), where n is the number of trials and p the probability of success in each

bino′mial distribu′tion


n.
a statistical distribution giving the probability of obtaining a specified number of successes in a finite set of independent trials in which the probability of a success remains the same from trial to trial.
[1910–15]
ThesaurusAntonymsRelated WordsSynonymsLegend:
Noun1.binomial distribution - a theoretical distribution of the number of successes in a finite set of independent trials with a constant probability of success
distribution, statistical distribution - (statistics) an arrangement of values of a variable showing their observed or theoretical frequency of occurrence
statistics - a branch of applied mathematics concerned with the collection and interpretation of quantitative data and the use of probability theory to estimate population parameters
References in periodicals archive ?
For Mixon, the negative binomial distribution with q = 2.
The ridge patterns were analyzed using binomial distribution with regard to the homogeneity of these patterns.
The k parameter of the negative binomial distribution estimated by the maximum likelihood method is calculated iteratively and is the value that equates the two members of the following (Bliss and Fisher, 1953):
Definition: A random variable X is said to follow Binomial distribution, if it assumes only nonnegative values and its probability mass function is given by,
The use of the negative binomial distribution for the outcome variable ensures that overdispersion (where the variance of the outcome variable is greater than the mean) is also accounted for, something which the Poisson distribution does not do.
1) is used to describe the transition between 2 states, a binomial distribution is often assumed (e.
This example shows that the binomial distribution can arise in the context of life tables constructed from Australian data.
6] suggested the modified median unbiased estimator (MMUE) in two independent binomial distributions.
Clearly X has binomial distribution with parameters n and 1 - a.
It is more appropriate to use logistic regression for the classification, where response is assumed to follow the binomial distribution.
We next fit the negative binomial distribution to the transmission data and to various data subsets according to patients' circumstances.