Teoria probabilității opțiunilor binare
A discrete probability distribution is applicable to the scenarios where the set of possible outcomes is discrete e. On the other hand, continuous probability distributions are applicable to scenarios where the set of possible outcomes can take on values in a continuous range e. In this case, probabilities are typically described by a probability density function.
More complex experiments, such as those involving stochastic processes defined in continuous timemay demand the use of more general probability measures. A probability distribution whose sample space is one-dimensional for example real numbers, list of labels, ordered labels or binary is called univariatewhile a distribution whose sample space is a vector space of dimension 2 or more is called multivariate.
Glosar de statistica
A univariate distribution gives the probabilities of a single random variable taking on various alternative values; a multivariate distribution a joint probability distribution gives the probabilities of a random vector — a list of two or more random variables — taking on various combinations of values.
Important and commonly encountered univariate probability distributions include the binomial distributionthe hypergeometric distributionand the normal distribution.
A commonly encountered multivariate distribution is the multivariate normal distribution. Besides the probability function, the cumulative distribution function, the probability mass function and the probability density function, the moment generating function and the characteristic function also teoria probabilității opțiunilor binare to identify a probability distribution, as they uniquely determine an underlying cumulative distribution function.
As notated on the figure, the probabilities of intervals of values correspond to the area under the curve. Terminology[ edit ] Some key concepts and terms, widely used in the literature on the topic of probability distributions, are listed below.