Interface BivariateProbabilityDistribution
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- 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.
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Method Summary
All Methods Instance Methods Abstract 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
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Method Detail
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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 \(X_2\)
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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 \(X_2\)
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