SSJ
V. 2.6.

## umontreal.iro.lecuyer.probdistmulti Class ContinuousDistributionMulti

```java.lang.Object
umontreal.iro.lecuyer.probdistmulti.ContinuousDistributionMulti
```
Direct Known Subclasses:
ContinuousDistribution2Dim, DirichletDist, MultiNormalDist

`public abstract class ContinuousDistributionMultiextends 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. 2.6.

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