SSJ
V. 1.2.5.

umontreal.iro.lecuyer.probdistmulti
Class ContinuousDistributionMulti

java.lang.Object
  extended by 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,…, xd); 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,…, xd), the probability density of X evaluated at the point x, where x = {x1, x2,…, xd}. The convention is that x[i - 1] = xi.

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 μi = E[Xi].


getCovariance

public abstract double[][] getCovariance()
Returns the variance-covariance matrix of the distribution, defined as
σij = E[(Xi - μi)(Xj - μj)].


getCorrelation

public abstract double[][] getCorrelation()
Returns the correlation matrix of the distribution, defined as ρij = σij/(σ_iiσ_jj)1/2.


SSJ
V. 1.2.5.

To submit a bug or ask questions, send an e-mail to Pierre L'Ecuyer.