# MultivariateGaussian

### Related Doc: package distribution

#### class MultivariateGaussian extends 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))

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@Since( "1.3.0" ) @DeveloperApi()
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MultivariateGaussian.scala
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### Instance Constructors

1. #### new MultivariateGaussian(mu: Vector, sigma: Matrix)

mu

The mean vector of the distribution

sigma

The covariance matrix of the distribution

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@Since( "1.3.0" )

### Value Members

1. #### final def !=(arg0: Any): Boolean

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2. #### final def ##(): Int

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3. #### final def ==(arg0: Any): Boolean

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5. #### def clone(): AnyRef

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7. #### def equals(arg0: Any): Boolean

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8. #### def finalize(): Unit

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10. #### def hashCode(): Int

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11. #### final def isInstanceOf[T0]: Boolean

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12. #### def logpdf(x: Vector): Double

Returns the log-density of this multivariate Gaussian at given point, x

Returns the log-density of this multivariate Gaussian at given point, x

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@Since( "1.3.0" )
13. #### val mu: Vector

The mean vector of the distribution

The mean vector of the distribution

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@Since( "1.3.0" )
14. #### final def ne(arg0: AnyRef): Boolean

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15. #### final def notify(): Unit

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16. #### final def notifyAll(): Unit

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17. #### def pdf(x: Vector): Double

Returns density of this multivariate Gaussian at given point, x

Returns density of this multivariate Gaussian at given point, x

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@Since( "1.3.0" )
18. #### val sigma: Matrix

The covariance matrix of the distribution

The covariance matrix of the distribution

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@Since( "1.3.0" )
19. #### final def synchronized[T0](arg0: ⇒ T0): T0

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20. #### def toString(): String

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21. #### final def wait(): Unit

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