Constructor Detail

• MultivariateNormalDistribution

  public MultivariateNormalDistribution(double[] means,
                                double[][] covariances)
                                 throws SingularMatrixException,
                                        DimensionMismatchException,
                                        NonPositiveDefiniteMatrixException

  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:

  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.

• MultivariateNormalDistribution

  public MultivariateNormalDistribution(RandomGenerator rng,
                                double[] means,
                                double[][] covariances)
                                 throws SingularMatrixException,
                                        DimensionMismatchException,
                                        NonPositiveDefiniteMatrixException

  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:

  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.

Method Detail

• getMeans

  public double[] getMeans()

  Gets the mean vector.

  Returns:

  the mean vector.

• getCovariances

  public RealMatrix getCovariances()

  Gets the covariance matrix.

  Returns:

  the covariance matrix.

• density

  public double density(double[] vals)
                 throws DimensionMismatchException

  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:

  DimensionMismatchException

• 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.