org.hipparchus.distribution

## Interface MultivariateRealDistribution

• ### Method Summary

All Methods
Modifier and Type Method and Description
double density(double[] x)
Returns the probability density function (PDF) of this distribution evaluated at the specified point x.
int getDimension()
Gets the number of random variables of the distribution.
void reseedRandomGenerator(long seed)
Reseeds the random generator used to generate samples.
double[] sample()
Generates a random value vector sampled from this distribution.
double[][] sample(int sampleSize)
Generates a list of a random value vectors from the distribution.
• ### Method Detail

• #### density

double density(double[] x)
Returns the probability density function (PDF) of this distribution evaluated at the specified point x. In general, the PDF is the derivative of the cumulative distribution function. If the derivative does not exist at x, then an appropriate replacement should be returned, e.g. Double.POSITIVE_INFINITY, Double.NaN, or the limit inferior or limit superior of the difference quotient.
Parameters:
x - Point at which the PDF is evaluated.
Returns:
the value of the probability density function at point x.
• #### reseedRandomGenerator

void reseedRandomGenerator(long seed)
Reseeds the random generator used to generate samples.
Parameters:
seed - Seed with which to initialize the random number generator.
• #### getDimension

int getDimension()
Gets the number of random variables of the distribution. It is the size of the array returned by the sample method.
Returns:
the number of variables.
• #### sample

double[] sample()
Generates a random value vector sampled from this distribution.
Returns:
a random value vector.
• #### sample

double[][] sample(int sampleSize)
throws MathIllegalArgumentException
Generates a list of a random value vectors from the distribution.
Parameters:
sampleSize - the number of random vectors to generate.
Returns:
an array representing the random samples.
Throws:
MathIllegalArgumentException - if sampleSize is not positive.
sample()