Class/Object

au.id.cxd.math.probability.discrete

TableDistribution

Related Docs: object TableDistribution | package discrete

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class TableDistribution extends DiscreteDistribution

A table distribution represents a discrete collection of values which correspond to a discrete distribution.

The table must have at least two columns.

The domainColumn contains values of Y that map to the row associated with f(y).

The rangeColumn is the column contains the P(Y) values, all values of this column must sum to 1.

Created by cd on 26/03/2016.

Linear Supertypes
DiscreteDistribution, Distribution, AnyRef, Any
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Inherited
  1. TableDistribution
  2. DiscreteDistribution
  3. Distribution
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Instance Constructors

  1. new TableDistribution(table: DenseMatrix[Double], domainColumn: Int = 0, rangeColumn: Int = 1)

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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. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any

  5. def cdf(range: Seq[Double]): Double

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    cumulative distribution function

    cumulative distribution function

    Definition Classes
    DiscreteDistribution → Distribution

  6. def clone(): AnyRef

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

  7. val domainColumn: Int

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

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    Definition Classes
    AnyRef

  9. def equals(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any

  10. def finalize(): Unit

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

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

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    Definition Classes
    AnyRef → Any

  12. def hashCode(): Int

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    Definition Classes
    AnyRef → Any

  13. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any

  14. def mean(): Double

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    approximate empircal mean of the values in Y

    approximate empircal mean of the values in Y

    Definition Classes
    TableDistribution → Distribution

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

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    Definition Classes
    AnyRef

  16. final def notify(): Unit

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    Definition Classes
    AnyRef

  17. final def notifyAll(): Unit

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    Definition Classes
    AnyRef

  18. def pdf(y: Double): Double

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    lookup the p-value corresponding to the value of Y.

    lookup the p-value corresponding to the value of Y.

    Definition Classes
    TableDistribution → Distribution

  19. val rangeColumn: Int

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  20. def rowFor(y: Double): Int

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    lookup the row for the value y the assumption is that Y exists in the table.

  21. def stddev(): Double

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    standard deviation $\sigma$

    standard deviation $\sigma$

    Definition Classes
    Distribution

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

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    Definition Classes
    AnyRef

  23. val table: DenseMatrix[Double]

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  24. def toString(): String

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    Definition Classes
    AnyRef → Any

  25. def variance(): Double

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    the approximate standard deviation

    the approximate standard deviation

    Definition Classes
    TableDistribution → Distribution

  26. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from DiscreteDistribution

Inherited from Distribution

Inherited from AnyRef

Inherited from Any

Ungrouped