Class

io.github.mandar2812.dynaml.probability.distributions

UESN

Related Doc: package distributions

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case class UESN(tau: Double, alpha: Double, mu: Double = 0.0, sigma: Double = 1.0)(implicit rand: RandBasis = Rand) extends SkewSymmDistribution[Double] with Product with Serializable

The univariate version of MESN of Adcock and Schutes.

See also: MESN

Linear Supertypes
Product, Equals, SkewSymmDistribution[Double], ContinuousDistr[Double], Rand[Double], Serializable, Serializable, Density[Double], AnyRef, Any
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Inherited
  1. UESN
  2. Product
  3. Equals
  4. SkewSymmDistribution
  5. ContinuousDistr
  6. Rand
  7. Serializable
  8. Serializable
  9. Density
  10. AnyRef
  11. Any
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Visibility
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Instance Constructors

  1. new UESN(tau: Double, alpha: Double, mu: Double = 0.0, sigma: Double = 1.0)(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. val alpha: Double

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  5. def apply(x: Double): Double

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    Definition Classes
    ContinuousDistr → Density

  6. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any

  7. val basisDistr: ContinuousDistr[Double]

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    Attributes
    protected
    Definition Classes
    UESN → SkewSymmDistribution

  8. def clone(): AnyRef

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

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

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    Definition Classes
    Rand

  10. val cutoff: Double

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    Attributes
    protected
    Definition Classes
    UESN → SkewSymmDistribution

  11. def draw(): Double

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    Definition Classes
    UESN → SkewSymmDistribution → Rand

  12. def drawOpt(): Option[Double]

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    Definition Classes
    Rand

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

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    Definition Classes
    AnyRef

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

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    Definition Classes
    Rand

  17. def foreach(f: (Double) ⇒ Unit): Unit

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    Definition Classes
    Rand

  18. def get(): Double

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    Definition Classes
    Rand

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

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    Definition Classes
    AnyRef → Any

  20. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any

  21. def logApply(x: Double): Double

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    Definition Classes
    ContinuousDistr → Density

  22. def logNormalizer: Double

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    Definition Classes
    SkewSymmDistribution → ContinuousDistr

  23. def logPdf(x: Double): Double

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    Definition Classes
    ContinuousDistr

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

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    Definition Classes
    Rand

  25. val mu: 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: Double): Double

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    Definition Classes
    ContinuousDistr

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

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    Definition Classes
    Rand

  32. def sample(): Double

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    Definition Classes
    Rand

  33. def samples: Iterator[Double]

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    Definition Classes
    Rand

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

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    Definition Classes
    Rand

  35. val sigma: Double

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

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    Definition Classes
    AnyRef

  37. val tau: Double

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  38. def unnormalizedLogPdf(x: Double): Double

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    Definition Classes
    SkewSymmDistribution → ContinuousDistr

  39. def unnormalizedPdf(x: Double): Double

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    Definition Classes
    ContinuousDistr

  40. val w: DataPipe[Double, Double]

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    Attributes
    protected
    Definition Classes
    UESN → SkewSymmDistribution

  41. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  44. val warped_cutoff: Double

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    The warped cutoff is an adjusted value of the cutoff based on the cutoff value and skewness parameters.

    The warped cutoff is an adjusted value of the cutoff based on the cutoff value and skewness parameters. To be implemented by extending class.

    Attributes
    protected
    Definition Classes
    UESN → SkewSymmDistribution

  45. val warpingDistr: ContinuousDistr[Double] with HasCdf

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    Attributes
    protected
    Definition Classes
    UESN → SkewSymmDistribution

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

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    Definition Classes
    Rand

Inherited from Product

Inherited from Equals

Inherited from SkewSymmDistribution[Double]

Inherited from ContinuousDistr[Double]

Inherited from Rand[Double]

Inherited from Serializable

Inherited from Serializable

Inherited from Density[Double]

Inherited from AnyRef

Inherited from Any

Ungrouped