Class/Object

io.github.mandar2812.dynaml.probability.distributions

MVGaussian

Related Docs: object MVGaussian | package distributions

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class MVGaussian extends AbstractContinuousDistr[DenseVector[Double]] with Moments[DenseVector[Double], DenseMatrix[Double]] with HasErrorBars[DenseVector[Double]]

Linear Supertypes
HasErrorBars[DenseVector[Double]], Moments[DenseVector[Double], DenseMatrix[Double]], AbstractContinuousDistr[DenseVector[Double]], ContinuousDistr[DenseVector[Double]], GenericDistribution[DenseVector[Double]], Rand[DenseVector[Double]], Serializable, Serializable, Density[DenseVector[Double]], AnyRef, Any
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  2. By Inheritance
Inherited
  1. MVGaussian
  2. HasErrorBars
  3. Moments
  4. AbstractContinuousDistr
  5. ContinuousDistr
  6. GenericDistribution
  7. Rand
  8. Serializable
  9. Serializable
  10. Density
  11. AnyRef
  12. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new MVGaussian(mu: DenseVector[Double], covariance: 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. def confidenceInterval(s: Double): (DenseVector[Double], DenseVector[Double])

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    Definition Classes
    MVGaussian → HasErrorBars

  9. def draw(): DenseVector[Double]

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    Definition Classes
    MVGaussian → Rand

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

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    Definition Classes
    Rand

  11. def entropy: Double

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    Definition Classes
    MVGaussian → Moments

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

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    Definition Classes
    AnyRef

  13. def equals(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any

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

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    Definition Classes
    Rand

  15. def finalize(): Unit

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

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

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    Definition Classes
    Rand

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

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    Definition Classes
    Rand

  18. def get(): DenseVector[Double]

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    Definition Classes
    Rand

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

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    Definition Classes
    AnyRef → Any

  20. def hashCode(): Int

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    Definition Classes
    AnyRef → Any

  21. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any

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

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    Definition Classes
    ContinuousDistr → Density

  23. def logNormalizer: Double

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    Definition Classes
    MVGaussian → ContinuousDistr

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

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    Definition Classes
    ContinuousDistr

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

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    Definition Classes
    Rand

  26. def mean: DenseVector[Double]

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    Definition Classes
    MVGaussian → Moments

  27. def mode: DenseVector[Double]

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    Definition Classes
    MVGaussian → Moments

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

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    Definition Classes
    AnyRef

  29. lazy val normalizer: Double

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    Definition Classes
    ContinuousDistr

  30. final def notify(): Unit

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    Definition Classes
    AnyRef

  31. final def notifyAll(): Unit

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    Definition Classes
    AnyRef

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

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    Definition Classes
    ContinuousDistr

  33. lazy val root: DenseMatrix[Double]

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    Attributes
    protected

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

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    Definition Classes
    Rand

  35. def sample(): DenseVector[Double]

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    Definition Classes
    Rand

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

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    Definition Classes
    Rand

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

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    Definition Classes
    Rand

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

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    Definition Classes
    AnyRef

  39. def toString(): String

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    Definition Classes
    AnyRef → Any

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

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    Definition Classes
    MVGaussian → ContinuousDistr

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

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    Definition Classes
    ContinuousDistr

  42. def variance: DenseMatrix[Double]

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    Definition Classes
    MVGaussian → Moments

  43. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

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    Definition Classes
    Rand

Inherited from HasErrorBars[DenseVector[Double]]

Inherited from Moments[DenseVector[Double], DenseMatrix[Double]]

Inherited from AbstractContinuousDistr[DenseVector[Double]]

Inherited from ContinuousDistr[DenseVector[Double]]

Inherited from GenericDistribution[DenseVector[Double]]

Inherited from Rand[DenseVector[Double]]

Inherited from Serializable

Inherited from Serializable

Inherited from Density[DenseVector[Double]]

Inherited from AnyRef

Inherited from Any

Ungrouped