## Interface BivariateProbabilityDistribution

• All Superinterfaces:
MultivariateProbabilityDistribution
All Known Subinterfaces:
BivariateEVD
All Known Implementing Classes:
AbstractBivariateEVD, AbstractBivariateProbabilityDistribution, BivariateEVDAsymmetricLogistic, BivariateEVDAsymmetricMixed, BivariateEVDAsymmetricNegativeLogistic, BivariateEVDBilogistic, BivariateEVDColesTawn, BivariateEVDHuslerReiss, BivariateEVDLogistic, BivariateEVDNegativeBilogistic, BivariateEVDNegativeLogistic

public interface BivariateProbabilityDistribution
extends MultivariateProbabilityDistribution
A bivariate or joint probability distribution for X_1, X_2 is a probability distribution that gives the probability that each of X_1, X_2, ... falls in any particular range or discrete set of values specified for that variable.
Wikipedia: Joint probability distribution
• ### Method Summary

All Methods
Modifier and Type Method Description
double cdf​(double x1, double x2)
The joint distribution function $$F_{X_1,X_2}(x_1,x_2) = Pr(X_1 \le x_1, X_2 \le x_2)$$.
double density​(double x1, double x2)
The joint distribution density $$f_{X_1,X_2}(x_1,x_2)$$.
• ### Methods inherited from interface dev.nm.stat.distribution.multivariate.MultivariateProbabilityDistribution

cdf, covariance, density, entropy, mean, mode, moment
• ### Method Detail

• #### cdf

double cdf​(double x1,
double x2)
The joint distribution function $$F_{X_1,X_2}(x_1,x_2) = Pr(X_1 \le x_1, X_2 \le x_2)$$.
Parameters:
x1 - the value drawn from $$X_1$$
x2 - the value drawn from $$X_2$$
Returns:
the joint distribution of $$X_1$$ and $$X_2$$
• #### density

double density​(double x1,
double x2)
The joint distribution density $$f_{X_1,X_2}(x_1,x_2)$$.
Parameters:
x1 - the value drawn from $$X_1$$
x2 - the value drawn from $$X_2$$
Returns:
the joint density of $$X_1$$ and $$X_2$$