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A discrete probability distribution is the probability distribution of a random variable that can take on only a countable number of values [15] (almost surely) [16] which means that the probability of any event can be expressed as a (finite or countably infinite) sum: = (=), where is a countable set with () =.
The Boltzmann distribution, a discrete distribution important in statistical physics which describes the probabilities of the various discrete energy levels of a system in thermal equilibrium. It has a continuous analogue. Special cases include: The Gibbs distribution; The Maxwell–Boltzmann distribution; The Borel distribution
Distribution (or place) is one of the four elements of the marketing mix: the other three elements being product, pricing, and promotion. Decisions about distribution need to be taken in line with a company's overall strategic vision and mission. Developing a coherent distribution plan is a central component of strategic planning. At the ...
The notion of normal distribution, being one of the most important distributions in probability theory, has been extended far beyond the standard framework of the univariate (that is one-dimensional) case (Case 1). All these extensions are also called normal or Gaussian laws, so a certain ambiguity in names exists.
When one or more parameter(s) of a distribution are random variables, the compound distribution is the marginal distribution of the variable. Examples: If X | N is a binomial (N,p) random variable, where parameter N is a random variable with negative-binomial (m, r) distribution, then X is distributed as a negative-binomial (m, r/(p + qr)).
A different distribution is defined as that of the random variable defined, for a given constant μ, by (+). This random variable has a noncentral t-distribution with noncentrality parameter μ. This distribution is important in studies of the power of Student's t-test.
Ulbricht was surely wrong about that, Lynch said: "Reasonable people may and do disagree about the social utility of harsh sentences for the distribution of controlled substances, or even of ...
In statistics, a sampling distribution or finite-sample distribution is the probability distribution of a given random-sample-based statistic.If an arbitrarily large number of samples, each involving multiple observations (data points), were separately used in order to compute one value of a statistic (such as, for example, the sample mean or sample variance) for each sample, then the sampling ...