org.apache.spark.mllib.stat.distribution

## Class MultivariateGaussian

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

```public class MultivariateGaussian
extends Object
implements scala.Serializable```
:: DeveloperApi :: 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 Degenerate case in Multivariate normal distribution (Wikipedia))

param: mu The mean vector of the distribution param: sigma The covariance matrix of the distribution

Serialized Form
• ### Constructor Summary

Constructors
Constructor and Description
```MultivariateGaussian(Vector mu, Matrix sigma)```
• ### Method Summary

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

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

• #### MultivariateGaussian

```public MultivariateGaussian(Vector mu,
Matrix sigma)```
• ### Method Detail

• #### logpdf

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

`public Vector mu()`
• #### pdf

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

`public Matrix sigma()`