umontreal.iro.lecuyer.probdistmulti
Class ContinuousDistributionMulti

java.lang.Object
  umontreal.iro.lecuyer.probdistmulti.ContinuousDistributionMulti

Direct Known Subclasses:

ContinuousDistribution2Dim, DirichletDist, MultiNormalDist

public abstract class ContinuousDistributionMulti
extends Object

Classes implementing continuous multi-dimensional distributions should inherit from this class. Such distributions are characterized by a density function f (x₁, x₂,…, x_d); thus the signature of a density method is supplied here. All array indices start at 0.

Constructor Summary

ContinuousDistributionMulti()

Method Summary

abstract double	density(double[] x) Returns f (x1, x2,…, xd), the probability density of X evaluated at the point x, where x = {x1, x2,…, xd}.
abstract double[][]	getCorrelation() Returns the correlation matrix of the distribution, defined as ρij = σij/(σ_iiσ_jj)1/2.
abstract double[][]	getCovariance() Returns the variance-covariance matrix of the distribution, defined as σij = E[(Xi - μi)(Xj - μj)].
int	getDimension() Returns the dimension d of the distribution.
abstract double[]	getMean() Returns the mean vector of the distribution, defined as μi = E[Xi].

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

ContinuousDistributionMulti

public ContinuousDistributionMulti()

Method Detail

density

public abstract double density(double[] x)

Returns f (x₁, x₂,…, x_d), the probability density of X evaluated at the point x, where x = {x₁, x₂,…, x_d}. The convention is that x[i - 1] = x_i.

Parameters:

x - value at which the density is evaluated

Returns:

density function evaluated at x

getDimension

public int getDimension()

Returns the dimension d of the distribution.

getMean

public abstract double[] getMean()

Returns the mean vector of the distribution, defined as μ_i = E[X_i].

getCovariance

public abstract double[][] getCovariance()

Returns the variance-covariance matrix of the distribution, defined as
σ_ij = E[(X_i - μ_i)(X_j - μ_j)].

getCorrelation

public abstract double[][] getCorrelation()

Returns the correlation matrix of the distribution, defined as ρ_ij = σ_ij/(σ_iiσ_jj)^1/2.