Bivariate Normal Distribution
Multivariate normal distribution
In probability theory and statistics, the multivariate normal distribution or multivariate Gaussian distribution, is a generalization of the one-dimensional (univariatenormal distribution to higher dimensions. One possible definition is that a random vector is said to be p-variate normally distributed if every linear combination of its p components has a univariate normal distribution. However, its importance derives mainly from the Multivariate central limit theorem. The multivariate normal distribution is often used to describe, at least approximately, any set of (possibly) correlated real-valued random variables each of which clusters around a mean value.

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Bivariate Normal Distribution
Two variables follow the bivariate normal distribution if for each value of one variable, the corresponding values of another variable are normally distributed . The bivariate normal probability distribution function for a pair of continuous random variables (X and Y) is given by:

where
1, 2 are the respective means of the random variables X and Y
1, 2
is the correlation coefficient of X and Y
is the base of the natural logarithm, sometimes called Euler's e (2.71...)
is the constant Pi (3.14...)

bivariate normal distribution
B: (Matematika) sebaran normal dwipeubah

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