public class MultivariateNormalDistribution extends AbstractMultivariateRealDistribution
random| Constructor and Description | 
|---|
| MultivariateNormalDistribution(double[] means,
                              double[][] covariances)Creates a multivariate normal distribution with the given mean vector and
 covariance matrix. | 
| MultivariateNormalDistribution(RandomGenerator rng,
                              double[] means,
                              double[][] covariances)Creates a multivariate normal distribution with the given mean vector and
 covariance matrix. | 
| Modifier and Type | Method and Description | 
|---|---|
| double | density(double[] vals)Returns the probability density function (PDF) of this distribution
 evaluated at the specified point  x. | 
| RealMatrix | getCovariances()Gets the covariance matrix. | 
| double[] | getMeans()Gets the mean vector. | 
| double[] | getStandardDeviations()Gets the square root of each element on the diagonal of the covariance
 matrix. | 
| double[] | sample()Generates a random value vector sampled from this distribution. | 
getDimension, reseedRandomGenerator, samplepublic MultivariateNormalDistribution(double[] means,
                              double[][] covariances)
                               throws SingularMatrixException,
                                      DimensionMismatchException,
                                      NonPositiveDefiniteMatrixException
 Note: this constructor will implicitly create an instance of
 Well19937c as random generator to be used for sampling only (see
 sample() and AbstractMultivariateRealDistribution.sample(int)). In case no sampling is
 needed for the created distribution, it is advised to pass null
 as random generator via the appropriate constructors to avoid the
 additional initialisation overhead.
means - Vector of means.covariances - Covariance matrix.DimensionMismatchException - if the arrays length are
 inconsistent.SingularMatrixException - if the eigenvalue decomposition cannot
 be performed on the provided covariance matrix.NonPositiveDefiniteMatrixException - if any of the eigenvalues is
 negative.public MultivariateNormalDistribution(RandomGenerator rng, double[] means, double[][] covariances) throws SingularMatrixException, DimensionMismatchException, NonPositiveDefiniteMatrixException
rng - Random Number Generator.means - Vector of means.covariances - Covariance matrix.DimensionMismatchException - if the arrays length are
 inconsistent.SingularMatrixException - if the eigenvalue decomposition cannot
 be performed on the provided covariance matrix.NonPositiveDefiniteMatrixException - if any of the eigenvalues is
 negative.public double[] getMeans()
public RealMatrix getCovariances()
public double density(double[] vals)
               throws DimensionMismatchException
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.vals - Point at which the PDF is evaluated.x.DimensionMismatchExceptionpublic double[] getStandardDeviations()
public double[] sample()
sample in interface MultivariateRealDistributionsample in class AbstractMultivariateRealDistributionCopyright © 2003–2016 The Apache Software Foundation. All rights reserved.