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
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final
def
!=(arg0: Any): Boolean
- Definition Classes
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final
def
##(): Int
- Definition Classes
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final
def
==(arg0: Any): Boolean
- Definition Classes
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def
apply(x: DenseMatrix[Double]): Double
- Definition Classes
- ContinuousDistr → Density
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final
def
asInstanceOf[T0]: T0
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def
clone(): AnyRef
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def
condition(p: (DenseMatrix[Double]) ⇒ Boolean): Rand[DenseMatrix[Double]]
- Definition Classes
- Rand
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def
draw(): DenseMatrix[Double]
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
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def
drawOpt(): Option[DenseMatrix[Double]]
- Definition Classes
- Rand
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final
def
eq(arg0: AnyRef): Boolean
- Definition Classes
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def
filter(p: (DenseMatrix[Double]) ⇒ Boolean): Rand[DenseMatrix[Double]]
- Definition Classes
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def
finalize(): Unit
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def
flatMap[E](f: (DenseMatrix[Double]) ⇒ Rand[E]): Rand[E]
- Definition Classes
- Rand
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def
foreach(f: (DenseMatrix[Double]) ⇒ Unit): Unit
- Definition Classes
- Rand
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def
get(): DenseMatrix[Double]
- Definition Classes
- Rand
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final
def
getClass(): Class[_]
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final
def
isInstanceOf[T0]: Boolean
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def
logApply(x: DenseMatrix[Double]): Double
- Definition Classes
- ContinuousDistr → Density
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def
logNormalizer: Double
- Definition Classes
- MatrixNormal → ContinuousDistr
-
def
logPdf(x: DenseMatrix[Double]): Double
- Definition Classes
- ContinuousDistr
-
def
map[E](f: (DenseMatrix[Double]) ⇒ E): Rand[E]
- Definition Classes
- Rand
- val mu: DenseMatrix[Double]
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final
def
ne(arg0: AnyRef): Boolean
- Definition Classes
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lazy val
normalizer: Double
- Definition Classes
- ContinuousDistr
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final
def
notify(): Unit
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final
def
notifyAll(): Unit
- Definition Classes
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def
pdf(x: DenseMatrix[Double]): Double
- Definition Classes
- ContinuousDistr
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def
sample(n: Int): IndexedSeq[DenseMatrix[Double]]
- Definition Classes
- Rand
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def
sample(): DenseMatrix[Double]
- Definition Classes
- Rand
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def
samples: Iterator[DenseMatrix[Double]]
- Definition Classes
- Rand
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def
samplesVector[U >: DenseMatrix[Double]](size: Int)(implicit m: ClassTag[U]): DenseVector[U]
- Definition Classes
- Rand
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final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
- AnyRef
- val u: DenseMatrix[Double]
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def
unnormalizedLogPdf(x: DenseMatrix[Double]): Nothing
- Definition Classes
- MatrixNormal → ContinuousDistr
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def
unnormalizedPdf(x: DenseMatrix[Double]): Double
- Definition Classes
- ContinuousDistr
- val v: DenseMatrix[Double]
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final
def
wait(): Unit
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final
def
wait(arg0: Long, arg1: Int): Unit
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final
def
wait(arg0: Long): Unit
- Definition Classes
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def
withFilter(p: (DenseMatrix[Double]) ⇒ Boolean): Rand[DenseMatrix[Double]]
- Definition Classes
- Rand