Examples of using A random variable in English and their translations into Greek
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Colloquial
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Official
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Medicine
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Ecclesiastic
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Financial
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Official/political
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Computer
Suppose we have a random variable y.
If a random variable X has this distribution, we write X~ Exponential(λ).
Simply stated the value of a random variable is a numerical event.
Entropy quantities the uncertainty involved in predicting the value of a random variable.
The notion of a random variable, distribution functions, moments.
Here Θ is used to emphasize that the unknown value of θ is being treated as a random variable.
FDIST is calculated as FDIST=P(F<x),where F is a random variable that has an F distribution.
The variance of a random variable X is the expected value of the squared deviation from the mean of X, μ= E[X].
TINV is calculated as TINV= p(t<X),where X is a random variable that follows the t-distribution.
If X is a random variable with probability density function f, then the Laplace transform of f is given by the expectation.
TDIST is calculated as TDIST= p(x<abs(X)),where X is a random variable that follows the t-distribution.
For a random variable following this distribution, the expected value is then m1=(a+ b)/2 and the variance is m2- m12=(b- a)2/12.
Each oval shape represents a random variable that can adopt a number of values.
In probability theory and statistics, the moment-generating function of a random variable X is.
Each oval shape represents a random variable that can adopt any of a number of values.
As the machine cannot fill every cup with exactly 250g, the content added to individual cups shows some variation, andis considered a random variable x.
Thus, the distribution of a random variable X is discrete, and X is called a discrete random variable, if.
Markov's inequality(and other similar inequalities) relate probabilities to expectations, and provide(frequently loose butstill useful) bounds for the cumulative distribution function of a random variable.
In probability and statistics, a random variable is a variable whose value is subject to variations due to chance.
Being a function of random variables, the sample variance is itself a random variable, and it is natural to study its distribution.
RT=P(F>x), where F is a random variable that has an F distribution with deg_freedom1 and deg_freedom2 degrees of freedom.
In probability theory and statistics,variance is the expectation of the squared deviation of a random variable from its mean, and it informally measures how far a set of(random) numbers are spread out from their mean.
The second moment of a random variable attains the minimum value when taken around the first moment(i.e., mean) of the random variable, i.e. a r g m i n m E(( X- m) 2)= E( X){\displaystyle\mathrm{argmin}_{m}\,\mathrm{E}(( X-m)^{ 2})=\ mathrm{E}(X)\,}.
The variance of a random variable X{\ displaystyle X} is the expected value of the squared deviation from the mean of X{\ displaystyle X}, μ= E{\ displaystyle\ mu=\ operatorname{ E}}: Var( X)= E.{\ displaystyle\ operatorname{ Var}( X)=\ operatorname{ E}\ left.} This definition encompasses random variables that are generated by processes that are discrete, continuous, neither, or mixed.
For example, the standard deviation of a random variable which follows a Cauchy distribution is undefined because its expected value is undefined.
The special case of information entropy for a random variable with two outcomes is the binary entropy function, usually taken to the logarithmic base 2.
Each node in the graph presents a random variable while the edges between the nodes represent probabilistic dependencies among the corresponding random variables. .
FDIST is calculated as FDIST=P(F>x), where F is a random variable that has an F distribution with deg_freedom1 and deg_freedom2 degrees of freedom.
