Package org.apache.commons.math3.distribution

Class MultivariateNormalDistribution

    • Field Detail

      • means

        private final double[] means
        Vector of means.

      • covarianceMatrix

        private final RealMatrix covarianceMatrix
        Covariance matrix.

      • covarianceMatrixInverse

        private final RealMatrix covarianceMatrixInverse
        The matrix inverse of the covariance matrix.

      • covarianceMatrixDeterminant

        private final double covarianceMatrixDeterminant
        The determinant of the covariance matrix.

      • samplingMatrix

        private final RealMatrix samplingMatrix
        Matrix used in computation of samples.

    • 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.

    • 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.

      • getExponentTerm

        private double getExponentTerm​(double[] values)
        Computes the term used in the exponent (see definition of the distribution).
        Parameters:
        values - Values at which to compute density.
        Returns:
        the multiplication factor of density calculations.