org.apache.spark.ml.stat.distribution

## Class MultivariateGaussian

• java.lang.Object
• org.apache.spark.ml.stat.distribution.MultivariateGaussian
• All Implemented Interfaces:
java.io.Serializable

```public class MultivariateGaussian
extends java.lang.Object
implements scala.Serializable```
This class provides basic functionality for a Multivariate Gaussian (Normal) Distribution. In the event that the covariance matrix is singular, the density will be computed in a reduced dimensional subspace under which the distribution is supported. (see `http://en.wikipedia.org/wiki/Multivariate_normal_distribution#Degenerate_case`)

param: mean The mean vector of the distribution param: cov The covariance matrix of the distribution

Serialized Form
• ### Constructor Summary

Constructors
Constructor and Description
```MultivariateGaussian(Vector mean, Matrix cov)```
• ### Method Summary

Methods
Modifier and Type Method and Description
`Matrix` `cov()`
`double` `logpdf(Vector x)`
Returns the log-density of this multivariate Gaussian at given point, x
`Vector` `mean()`
`double` `pdf(Vector x)`
Returns density of this multivariate Gaussian at given point, x
• ### Methods inherited from class java.lang.Object

`clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`
• ### Constructor Detail

• #### MultivariateGaussian

```public MultivariateGaussian(Vector mean,
Matrix cov)```
• ### Method Detail

• #### mean

`public Vector mean()`
• #### cov

`public Matrix cov()`
• #### pdf

`public double pdf(Vector x)`
Returns density of this multivariate Gaussian at given point, x
Parameters:
`x` - (undocumented)
Returns:
(undocumented)
• #### logpdf

`public double logpdf(Vector x)`
Returns the log-density of this multivariate Gaussian at given point, x
Parameters:
`x` - (undocumented)
Returns:
(undocumented)