SSJ
V. 2.6.

## umontreal.iro.lecuyer.probdistmulti Class DiscreteDistributionIntMulti

```java.lang.Object
umontreal.iro.lecuyer.probdistmulti.DiscreteDistributionIntMulti
```
Direct Known Subclasses:
MultinomialDist, NegativeMultinomialDist

`public abstract class DiscreteDistributionIntMultiextends Object`

Classes implementing multi-dimensional discrete distributions over the integers should inherit from this class. It specifies the signature of methods for computing the mass function (or probability) p(x1, x2,…, xd) = P[X1 = x1, X2 = x2,…, Xd = xd] and the cumulative probabilities for a random vector X with a discrete distribution over the integers.

Constructor Summary
`DiscreteDistributionIntMulti()`

Method Summary
` double` `cdf(int[] x)`
Computes the cumulative probability function F of the distribution evaluated at x, assuming the lowest values start at 0, i.e.
`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].
`abstract  double` `prob(int[] x)`
Returns the probability mass function p(x1, x2,…, xd), which should be a real number in [0, 1].

Methods inherited from class java.lang.Object
`equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`

Constructor Detail

### DiscreteDistributionIntMulti

`public DiscreteDistributionIntMulti()`
Method Detail

### prob

`public abstract double prob(int[] x)`
Returns the probability mass function p(x1, x2,…, xd), which should be a real number in [0, 1].

Parameters:
`x` - value at which the mass function must be evaluated
Returns:
the mass function evaluated at x

### cdf

`public double cdf(int[] x)`
Computes the cumulative probability function F of the distribution evaluated at x, assuming the lowest values start at 0, i.e. computes

F(x1, x2,…, xd) = ∑s1=0x1s2=0x2 ... sd=0xdp(s1, s2,…, sd).

Uses the naive implementation, is very inefficient and may underflows.

### 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.