Package org.hipparchus.distribution.multivariate

Class MultivariateNormalDistribution

java.lang.Object
org.hipparchus.distribution.multivariate.AbstractMultivariateRealDistribution
org.hipparchus.distribution.multivariate.MultivariateNormalDistribution
All Implemented Interfaces:
MultivariateRealDistribution
public class MultivariateNormalDistribution extends AbstractMultivariateRealDistribution
Implementation of the multivariate normal (Gaussian) distribution.
See Also:

  • Constructor Details

    • MultivariateNormalDistribution

      public MultivariateNormalDistribution(double[] means, double[][] covariances) throws MathIllegalArgumentException
      Creates a multivariate normal distribution with the given mean vector and covariance matrix.
      The number of dimensions is equal to the length of the mean vector and to the number of rows and columns of the covariance matrix. It is frequently written as "p" in formulae.

      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.

      Parameters:
      means - Vector of means.
      covariances - Covariance matrix.
      Throws:
      MathIllegalArgumentException - if the arrays length are inconsistent.
      MathIllegalArgumentException - if the eigenvalue decomposition cannot be performed on the provided covariance matrix.
      MathIllegalArgumentException - if any of the eigenvalues is negative.

    • MultivariateNormalDistribution

      public MultivariateNormalDistribution(double[] means, double[][] covariances, double singularMatrixCheckTolerance) throws MathIllegalArgumentException
      Creates a multivariate normal distribution with the given mean vector and covariance matrix.
      The number of dimensions is equal to the length of the mean vector and to the number of rows and columns of the covariance matrix. It is frequently written as "p" in formulae.

      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.

      Parameters:
      means - Vector of means.
      covariances - Covariance matrix.
      singularMatrixCheckTolerance - Tolerance used during the singular matrix check before inversion
      Throws:
      MathIllegalArgumentException - if the arrays length are inconsistent.
      MathIllegalArgumentException - if the eigenvalue decomposition cannot be performed on the provided covariance matrix.
      MathIllegalArgumentException - if any of the eigenvalues is negative.

    • MultivariateNormalDistribution

      public MultivariateNormalDistribution(RandomGenerator rng, double[] means, double[][] covariances)
      Creates a multivariate normal distribution with the given mean vector and covariance matrix.
      The number of dimensions is equal to the length of the mean vector and to the number of rows and columns of the covariance matrix. It is frequently written as "p" in formulae.
      Parameters:
      rng - Random Number Generator.
      means - Vector of means.
      covariances - Covariance matrix.
      Throws:
      MathIllegalArgumentException - if the arrays length are inconsistent.
      MathIllegalArgumentException - if the eigenvalue decomposition cannot be performed on the provided covariance matrix.
      MathIllegalArgumentException - if any of the eigenvalues is negative.

    • MultivariateNormalDistribution

      public MultivariateNormalDistribution(RandomGenerator rng, double[] means, double[][] covariances, double singularMatrixCheckTolerance) throws MathIllegalArgumentException
      Creates a multivariate normal distribution with the given mean vector and covariance matrix.
      The number of dimensions is equal to the length of the mean vector and to the number of rows and columns of the covariance matrix. It is frequently written as "p" in formulae.
      Parameters:
      rng - Random Number Generator.
      means - Vector of means.
      covariances - Covariance matrix.
      singularMatrixCheckTolerance - Tolerance used during the singular matrix check before inversion
      Throws:
      MathIllegalArgumentException - if the arrays length are inconsistent.
      MathIllegalArgumentException - if the eigenvalue decomposition cannot be performed on the provided covariance matrix.
      MathIllegalArgumentException - if any of the eigenvalues is negative.

  • Method Details

    • getMeans

      public double[] getMeans()
      Gets the mean vector.
      Returns:
      the mean vector.

    • getCovariances

      public RealMatrix getCovariances()
      Gets the covariance matrix.
      Returns:
      the covariance matrix.

    • getSingularMatrixCheckTolerance

      public double getSingularMatrixCheckTolerance()
      Gets the current setting for the tolerance check used during singular checks before inversion
      Returns:
      tolerance

    • density

      public double density(double[] vals) throws MathIllegalArgumentException
      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:
      vals - Point at which the PDF is evaluated.
      Returns:
      the value of the probability density function at point x.
      Throws:
      MathIllegalArgumentException

    • getStandardDeviations

      public double[] getStandardDeviations()
      Gets the square root of each element on the diagonal of the covariance matrix.
      Returns:
      the standard deviations.

    • sample

      public double[] sample()
      Generates a random value vector sampled from this distribution.
      Specified by:
      sample in interface MultivariateRealDistribution
      Specified by:
      sample in class AbstractMultivariateRealDistribution
      Returns:
      a random value vector.