Class

core.dlm.model

MatrixNormal

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case class MatrixNormal(mu: DenseMatrix[Double], u: DenseMatrix[Double], v: DenseMatrix[Double])(implicit rand: RandBasis = Rand) extends ContinuousDistr[DenseMatrix[Double]] with Product with Serializable

A Normal distribution over matrices

mu

the location of the distribution

u

the variance of the rows

v

the variance of the columns

Linear Supertypes
Product, Equals, ContinuousDistr[DenseMatrix[Double]], Rand[DenseMatrix[Double]], Serializable, Serializable, Density[DenseMatrix[Double]], AnyRef, Any
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Inherited
  1. MatrixNormal
  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 MatrixNormal(mu: DenseMatrix[Double], u: DenseMatrix[Double], v: DenseMatrix[Double])(implicit rand: RandBasis = Rand)

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    mu

    the location of the distribution

    u

    the variance of the rows

    v

    the variance of the columns

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: DenseMatrix[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: (DenseMatrix[Double]) ⇒ Boolean): Rand[DenseMatrix[Double]]

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    Definition Classes
    Rand

  8. def draw(): DenseMatrix[Double]

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    Draw from a matrix normal distribution using the cholesky decomposition of the row and column covariance matrices

    Draw from a matrix normal distribution using the cholesky decomposition of the row and column covariance matrices

    Definition Classes
    MatrixNormal → Rand

  9. def drawOpt(): Option[DenseMatrix[Double]]

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    Definition Classes
    Rand

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

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    Definition Classes
    AnyRef

  11. def filter(p: (DenseMatrix[Double]) ⇒ Boolean): Rand[DenseMatrix[Double]]

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    Definition Classes
    Rand

  12. def finalize(): Unit

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

  13. def flatMap[E](f: (DenseMatrix[Double]) ⇒ Rand[E]): Rand[E]

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    Definition Classes
    Rand

  14. def foreach(f: (DenseMatrix[Double]) ⇒ Unit): Unit

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    Definition Classes
    Rand

  15. def get(): DenseMatrix[Double]

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    Definition Classes
    Rand

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

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    Definition Classes
    AnyRef → Any

  17. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any

  18. def logApply(x: DenseMatrix[Double]): Double

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    Definition Classes
    ContinuousDistr → Density

  19. def logNormalizer: Double

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    Definition Classes
    MatrixNormal → ContinuousDistr

  20. def logPdf(x: DenseMatrix[Double]): Double

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    Definition Classes
    ContinuousDistr

  21. def map[E](f: (DenseMatrix[Double]) ⇒ E): Rand[E]

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    Definition Classes
    Rand

  22. val mu: DenseMatrix[Double]

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    the location of the distribution

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

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    Definition Classes
    AnyRef

  24. lazy val normalizer: Double

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    Definition Classes
    ContinuousDistr

  25. final def notify(): Unit

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    Definition Classes
    AnyRef

  26. final def notifyAll(): Unit

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    Definition Classes
    AnyRef

  27. def pdf(x: DenseMatrix[Double]): Double

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    Definition Classes
    ContinuousDistr

  28. def sample(n: Int): IndexedSeq[DenseMatrix[Double]]

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    Definition Classes
    Rand

  29. def sample(): DenseMatrix[Double]

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    Definition Classes
    Rand

  30. def samples: Iterator[DenseMatrix[Double]]

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    Definition Classes
    Rand

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

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    Definition Classes
    Rand

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

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    Definition Classes
    AnyRef

  33. val u: DenseMatrix[Double]

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    the variance of the rows

  34. def unnormalizedLogPdf(x: DenseMatrix[Double]): Nothing

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    Definition Classes
    MatrixNormal → ContinuousDistr

  35. def unnormalizedPdf(x: DenseMatrix[Double]): Double

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    Definition Classes
    ContinuousDistr

  36. val v: DenseMatrix[Double]

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    the variance of the columns

  37. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  40. def withFilter(p: (DenseMatrix[Double]) ⇒ Boolean): Rand[DenseMatrix[Double]]

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    Definition Classes
    Rand

Inherited from Product

Inherited from Equals

Inherited from ContinuousDistr[DenseMatrix[Double]]

Inherited from Rand[DenseMatrix[Double]]

Inherited from Serializable

Inherited from Serializable

Inherited from Density[DenseMatrix[Double]]

Inherited from AnyRef

Inherited from Any

Ungrouped