MultivariateGaussian

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)

Source: MultivariateGaussian.scala

Linear Supertypes

Serializable, Serializable, AnyRef, Any

Instance Constructors

new MultivariateGaussian(mean: Vector, cov: Matrix)

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

Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def asInstanceOf[T0]: T0

Definition Classes
Any
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
val cov: Matrix

The covariance matrix of the distribution
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
def hashCode(): Int

Definition Classes
AnyRef → Any
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
def logpdf(x: Vector): Double

Returns the log-density of this multivariate Gaussian at given point, x
val mean: Vector

The mean vector of the distribution
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
def pdf(x: Vector): Double

Returns density of this multivariate Gaussian at given point, x
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
AnyRef → Any
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )

Related Doc: package distribution

class MultivariateGaussian extends Serializable

Instance Constructors

new MultivariateGaussian(mean: Vector, cov: Matrix)

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

val cov: Matrix

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

def logpdf(x: Vector): Double

val mean: Vector

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def pdf(x: Vector): Double

final def synchronized[T0](arg0: ⇒ T0): T0

def toString(): String

final def wait(): Unit

final def wait(arg0: Long, arg1: Int): Unit

final def wait(arg0: Long): Unit

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Members