Bernoulli trial


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Related to Bernoulli trial: binomial distribution, Geometric distribution

Bernoulli trial

n. Statistics
An experiment having only two possible outcomes, usually denoted success and failure, with the properties that the probability of occurrence of each outcome is the same in each trial and the occurrence of one excludes the occurrence of the other in any given trial.

[After Jakob Bernoulli.]

Bernoulli trial

n
(Statistics) statistics one of a sequence of independent experiments each of which has the same probability of success, such as successive throws of a die, the outcome of which is described by a binomial distribution. See also binomial experiment, geometric distribution
[named after Jacques Bernoulli]
References in periodicals archive ?
Bernoulli trials for independent random events is the basis of the binomial testing and the discrete probability distribution generated from the Bernoulli trial is the binomial distribution.
We consider a sequence of Bernoulli trial, and suppose that at each trial the bettor has the free choice of whether or not to bet.
The third chapter of Baxter's Handbook of Old Chinese (Berlin: Mouton de Gruyter, 1992) demonstrates, clearly but with no superfluous detail, how to use the probabilistic method called the Bernoulli trial to study rhyming in a received Chinese corpus.
Specific topics described include injectivity of the Dubins-Freedman construction of random distributions, almost sure convergence of weighted sums of independent random variables, aperiodic order via dynamical systems, laws of iterated logarithm for weighted sums of iid random variables, and homeomorphic Bernoulli trial measures and ergodic theory.
An event with dichotomous outcomes is typically modeled by a Bernoulli trial. For a well-designed test, the chance of misgrading (p) is not high.
We begin by modeling the survival or failure of a nest over an interval t as a Bernoulli trial with parameter [p.sup.t]: P(Y = y\p) = [([p.sup.t]).sup.y] [(1 - [p.sup.t]).sup.1 - y], where y is defined above and p is the daily survival probability of the nest.
As we mentioned in Section 2.2., a Standard Uniform U(0,1) variable can be used to model a binary process, that is, a Bernoulli trial. Let p [member of] (0, 1) be fixed and let U [member of] U(0,1).