Class

dlm.model

MultivariateGaussianSvd

Related Doc: package model

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case class MultivariateGaussianSvd(mu: DenseVector[Double], cov: DenseMatrix[Double])(implicit rand: RandBasis = Rand) extends ContinuousDistr[DenseVector[Double]] with Product with Serializable

Linear Supertypes
Product, Equals, ContinuousDistr[DenseVector[Double]], Rand[DenseVector[Double]], Serializable, Serializable, Density[DenseVector[Double]], AnyRef, Any
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Inherited
  1. MultivariateGaussianSvd
  2. Product
  3. Equals
  4. ContinuousDistr
  5. Rand
  6. Serializable
  7. Serializable
  8. Density
  9. AnyRef
  10. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new MultivariateGaussianSvd(mu: DenseVector[Double], cov: DenseMatrix[Double])(implicit rand: RandBasis = Rand)

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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. def apply(x: DenseVector[Double]): Double

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    Definition Classes
    ContinuousDistr → Density

  5. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any

  6. def clone(): AnyRef

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

  7. def condition(p: (DenseVector[Double]) ⇒ Boolean): Rand[DenseVector[Double]]

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    Definition Classes
    Rand

  8. val cov: DenseMatrix[Double]

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  9. def draw(): DenseVector[Double]

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    Draw from a multivariate gaussian using eigen decomposition which is often more stable than using the cholesky decomposition

    Draw from a multivariate gaussian using eigen decomposition which is often more stable than using the cholesky decomposition

    Definition Classes
    MultivariateGaussianSvd → Rand

  10. def drawOpt(): Option[DenseVector[Double]]

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    Definition Classes
    Rand

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

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    Definition Classes
    AnyRef

  12. def filter(p: (DenseVector[Double]) ⇒ Boolean): Rand[DenseVector[Double]]

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    Definition Classes
    Rand

  13. def finalize(): Unit

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

  14. def flatMap[E](f: (DenseVector[Double]) ⇒ Rand[E]): Rand[E]

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    Definition Classes
    Rand

  15. def foreach(f: (DenseVector[Double]) ⇒ Unit): Unit

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    Definition Classes
    Rand

  16. def get(): DenseVector[Double]

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    Definition Classes
    Rand

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

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    Definition Classes
    AnyRef → Any

  18. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any

  19. def logApply(x: DenseVector[Double]): Double

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    Definition Classes
    ContinuousDistr → Density

  20. def logNormalizer: Double

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    Definition Classes
    MultivariateGaussianSvd → ContinuousDistr

  21. def logPdf(x: DenseVector[Double]): Double

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    Definition Classes
    ContinuousDistr

  22. def map[E](f: (DenseVector[Double]) ⇒ E): Rand[E]

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    Definition Classes
    Rand

  23. def mean: DenseVector[Double]

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  24. def mode: DenseVector[Double]

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  25. val mu: DenseVector[Double]

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

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    Definition Classes
    AnyRef

  27. lazy val normalizer: Double

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    Definition Classes
    ContinuousDistr

  28. final def notify(): Unit

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    Definition Classes
    AnyRef

  29. final def notifyAll(): Unit

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    Definition Classes
    AnyRef

  30. def pdf(x: DenseVector[Double]): Double

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    Definition Classes
    ContinuousDistr

  31. def sample(n: Int): IndexedSeq[DenseVector[Double]]

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    Definition Classes
    Rand

  32. def sample(): DenseVector[Double]

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    Definition Classes
    Rand

  33. def samples: Iterator[DenseVector[Double]]

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    Definition Classes
    Rand

  34. def samplesVector[U >: DenseVector[Double]](size: Int)(implicit m: ClassTag[U]): DenseVector[U]

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    Definition Classes
    Rand

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

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    Definition Classes
    AnyRef

  36. def unnormalizedLogPdf(x: DenseVector[Double]): Double

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    Definition Classes
    MultivariateGaussianSvd → ContinuousDistr

  37. def unnormalizedPdf(x: DenseVector[Double]): Double

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    Definition Classes
    ContinuousDistr

  38. def variance: DenseMatrix[Double]

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  39. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  42. def withFilter(p: (DenseVector[Double]) ⇒ Boolean): Rand[DenseVector[Double]]

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    Definition Classes
    Rand

Inherited from Product

Inherited from Equals

Inherited from ContinuousDistr[DenseVector[Double]]

Inherited from Rand[DenseVector[Double]]

Inherited from Serializable

Inherited from Serializable

Inherited from Density[DenseVector[Double]]

Inherited from AnyRef

Inherited from Any

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