Examples of using Random variables in English and their translations into Italian
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Colloquial
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Medicine
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Ecclesiastic
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Ecclesiastic
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Computer
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Official/political
a sequence or collection of random variables is independent
for the generation of random variables and the analysis of random data samples.
is a random variable whose distribution is a Poisson distribution with expected value λ, and that: formula_2are identically distributed random variables that are mutually independent and also independent of"N.
which are applied to probability spaces and random variables, culminating in central limit theory.
the closure of the set of all linear combinations of these random variables in the Hilbert space of all square-integrable random variables on the given probability space.
connects convergence in distribution of the sequence of random variables with pointwise convergence of their characteristic functions.
grasp the significance of this quantity for a large number of random variables.
The usefulness of these depends on what is already known about the random variable; for example a random variable may be defined in terms of its probability density function or by construction from other random variables.
the data analysis being done, involves a wider set of random variables but that attention is being limited to a reduced number of those variables. .
In statistics, the probability integral transform or transformation relates to the result that data values that are modelled as being random variables from any given continuous distribution can be converted to random variables having a standard uniform distribution.
This holds exactly provided that the distribution being used is the true distribution of the random variables; if the distribution is one fitted to the data,
Probability. Random variables: probability function,
In many applications, an analysis may start with a given collection of random variables, then first extend the set by defining new ones(such as the sum of the original random variables)
as unit: :formula_21===Joint entropy===The joint entropy of two discrete random variables formula_7 and formula_23 is merely the entropy of their pairing: formula_24.
are i.i.d. random variables.
matrix whose(i, j)th element is the covariance between the i th and the j th random variables.
from each experiment separately: :formula_8This result follows from the elementary fact that if random variables are independent, the variance of their sum is the sum of their variances.
σ v 2){\displaystyleN(\mu,\sigma_{v}^{2})} random variables and a priori distribution of μ{\displaystyle\mu}
1}}}\ sin( 2\pi U_{2}).\,} Then Z0 and Z1 are independent random variables with a standard normal distribution.
often entrusted to random variables, or fragments of images on paper mounted on the lines of visual poetry.