Examples of using Random variables in English and their translations into Russian
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Colloquial
Examples of discrete and continuous random variables.
Random variables and random vectors 5 lectures.
Parameters can be represented as random variables.
Random variables whose covariance is zero are called uncorrelated.
We also suppose that all random variables are mutually independent.
Let 21, XX be independent geometric random variables.
Pairwise independent random variables with finite variance are uncorrelated.
Correlation- is a statistical relationship between two or more random variables.
Athanasios Papoulis' Probability, Random Variables, and Stochastic Processes.
A similar trend can be seen in our fancier simulation with two random variables.
Let 1 2,X X be independent Poisson random variables with parameters 1 2, respectively.
The technique consists in determining the distribution of R-R intervals as random variables.
Thus the trends among the above-listed random variables are multi-directional.
Consider three random variables A{\displaystyle A}, B{\displaystyle B}, and C{\displaystyle C.
In mathematics, the values of analyzed body parameters can be considered as random variables RV.
Convolution is used to add two independent random variables defined by distribution functions.
It is assumed throughout the thesis that various activities durations are mutually independent random variables kXXX,,, 21.
This assumes that two random variables are independent conditional on a third variable. .
The shifted Gompertz distribution is the distribution of the maximum of independent exponential andextreme-value distributed random variables.
If the process is Gaussian,then the random variables Zk are Gaussian and stochastically independent.
The random variables U a T X{\displaystyle U=a^{T}X} and V b T Y{\displaystyle V=b^{T}Y} are the first pair of canonical variables.
The distributions of each of the component random variables X i{\displaystyle X_{i}} are called marginal distributions.
Log-normal distributions are encountered in many fields, wherever a variable is formed as the product of many independent positive random variables, for example in the study of turbulence.
Mutual independence of the random variables can be replaced by pairwise independence in both versions of the law.
NMF can be seen as a two-layer directed graphical model with one layer of observed random variables and one layer of hidden random variables.
From descriptive statistics and random variables to time series and random processes, the whole framework is stronger, faster, and easier to use.
Keywords: programming in Pascal, graphics capabilities of PascalABC, processing arrays, motion graphics, finding minimum element,second maximum, random variables, sorting arrays, procedures in Pascal, two-dimensional arrays.
Moreover, it is possible to show that these two random variables(the normally distributed one Z and the chi-squared-distributed one V) are independent.
Keywords: information model, simulation,correlated random variables, model individual insurance, risk management.
Probability theory: random variables, their distributions, moments, characteristic functions, Chebyshev inequality, law of large numbers, central limit theorem.