Class/Object

scalismo.statisticalmodel

NDimensionalNormalDistribution

Related Docs: object NDimensionalNormalDistribution | package statisticalmodel

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case class NDimensionalNormalDistribution[D <: Dim](mean: Vector[D], cov: SquareMatrix[D])(implicit evidence$2: NDSpace[D]) extends MultivariateNormalDistributionLike[Vector[D], SquareMatrix[D]] with Product with Serializable

Linear Supertypes
Serializable, Serializable, Product, Equals, MultivariateNormalDistributionLike[Vector[D], SquareMatrix[D]], AnyRef, Any
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Inherited
  1. NDimensionalNormalDistribution
  2. Serializable
  3. Serializable
  4. Product
  5. Equals
  6. MultivariateNormalDistributionLike
  7. AnyRef
  8. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new NDimensionalNormalDistribution(mean: Vector[D], cov: SquareMatrix[D])(implicit arg0: NDSpace[D])

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Value Members

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

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    Definition Classes
    AnyRef → Any

  2. final def ##(): Int

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    Definition Classes
    AnyRef → Any

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

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    Definition Classes
    AnyRef → Any

  4. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any

  5. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  6. val cov: SquareMatrix[D]

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    Definition Classes
    NDimensionalNormalDistribution → MultivariateNormalDistributionLike

  7. def dim: Int

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    Definition Classes
    NDimensionalNormalDistribution → MultivariateNormalDistributionLike

  8. final def eq(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef

  9. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )

  10. final def getClass(): Class[_]

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    Definition Classes
    AnyRef → Any

  11. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any

  12. def logpdf(x: Vector[D]): Double

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    Definition Classes
    NDimensionalNormalDistribution → MultivariateNormalDistributionLike

  13. def mahalanobisDistance(x: Vector[D]): Double

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    Definition Classes
    NDimensionalNormalDistribution → MultivariateNormalDistributionLike

  14. val mean: Vector[D]

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    Definition Classes
    NDimensionalNormalDistribution → MultivariateNormalDistributionLike

  15. final def ne(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef

  16. final def notify(): Unit

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    Definition Classes
    AnyRef

  17. final def notifyAll(): Unit

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    Definition Classes
    AnyRef

  18. def pdf(x: Vector[D]): Double

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    Definition Classes
    NDimensionalNormalDistribution → MultivariateNormalDistributionLike

  19. def principalComponents: Seq[(Vector[D], Double)]

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    Definition Classes
    NDimensionalNormalDistribution → MultivariateNormalDistributionLike

  20. def sample(): Vector[D]

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    Definition Classes
    NDimensionalNormalDistribution → MultivariateNormalDistributionLike

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

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    Definition Classes
    AnyRef

  22. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  24. final def wait(arg0: Long): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from Serializable

Inherited from Serializable

Inherited from Product

Inherited from Equals

Inherited from MultivariateNormalDistributionLike[Vector[D], SquareMatrix[D]]

Inherited from AnyRef

Inherited from Any

Ungrouped