Class

breeze.stats.distributions

NegativeBinomial

Related Doc: package distributions

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case class NegativeBinomial(r: Double, p: Double) extends DiscreteDistr[Int] with Product with Serializable

Negative Binomial Distribution

r

number of failures until stop

p

prob of success

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Inherited
  1. NegativeBinomial
  2. Product
  3. Equals
  4. DiscreteDistr
  5. Rand
  6. Serializable
  7. Serializable
  8. Density
  9. AnyRef
  10. Any
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Instance Constructors

  1. new NegativeBinomial(r: Double, p: Double)

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    r

    number of failures until stop

    p

    prob of success

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: Int): Double

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    Returns the unnormalized value of the measure

    Returns the unnormalized value of the measure

    Definition Classes
    DiscreteDistrDensity
  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: (Int) ⇒ Boolean): Rand[Int]

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    Definition Classes
    Rand
  8. def draw(): Int

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    Gets one sample from the distribution.

    Gets one sample from the distribution. Equivalent to sample()

    Definition Classes
    NegativeBinomialRand
  9. def drawOpt(): Option[Int]

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    Overridden by filter/map/flatmap for monadic invocations.

    Overridden by filter/map/flatmap for monadic invocations. Basically, rejeciton samplers will return None here

    Definition Classes
    Rand
  10. final def eq(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  11. def filter(p: (Int) ⇒ Boolean): Rand[Int]

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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: (Int) ⇒ Rand[E]): Rand[E]

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    Converts a random sampler of one type to a random sampler of another type.

    Converts a random sampler of one type to a random sampler of another type. Examples: randInt(10).flatMap(x => randInt(3 * x.asInstanceOf[Int]) gives a Rand[Int] in the range [0,30] Equivalently, for(x <- randInt(10); y <- randInt(30 *x)) yield y

    f

    the transform to apply to the sampled value.

    Definition Classes
    Rand
  14. def foreach(f: (Int) ⇒ Unit): Unit

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    Samples one element and qpplies the provided function to it.

    Samples one element and qpplies the provided function to it. Despite the name, the function is applied once. Sample usage:

     for(x <- Rand.uniform) { println(x) } 
    

    f

    the function to be applied

    Definition Classes
    Rand
  15. def get(): Int

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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: Int): Double

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    Returns the log unnormalized value of the measure

    Returns the log unnormalized value of the measure

    Definition Classes
    DiscreteDistrDensity
  19. def logProbabilityOf(k: Int): Double

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    Definition Classes
    NegativeBinomialDiscreteDistr
  20. def map[E](f: (Int) ⇒ E): Rand[E]

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    Converts a random sampler of one type to a random sampler of another type.

    Converts a random sampler of one type to a random sampler of another type. Examples: uniform.map(_*2) gives a Rand[Double] in the range [0,2] Equivalently, for(x <- uniform) yield 2*x

    f

    the transform to apply to the sampled value.

    Definition Classes
    Rand
  21. final def ne(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  22. final def notify(): Unit

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    Definition Classes
    AnyRef
  23. final def notifyAll(): Unit

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    Definition Classes
    AnyRef
  24. val p: Double

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    prob of success

  25. def probabilityOf(x: Int): Double

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    Returns the probability of that draw.

    Returns the probability of that draw.

    Definition Classes
    NegativeBinomialDiscreteDistr
  26. val r: Double

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    number of failures until stop

  27. def sample(n: Int): IndexedSeq[Int]

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    Gets n samples from the distribution.

    Gets n samples from the distribution.

    Definition Classes
    Rand
  28. def sample(): Int

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    Gets one sample from the distribution.

    Gets one sample from the distribution. Equivalent to get()

    Definition Classes
    Rand
  29. def samples: Iterator[Int]

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    An infinitely long iterator that samples repeatedly from the Rand

    An infinitely long iterator that samples repeatedly from the Rand

    returns

    an iterator that repeatedly samples

    Definition Classes
    Rand
  30. def samplesVector[U >: Int](size: Int)(implicit m: ClassTag[U]): DenseVector[U]

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    Return a vector of samples.

    Return a vector of samples.

    Definition Classes
    Rand
  31. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
    AnyRef
  32. def unnormalizedLogProbabilityOf(x: Int): Double

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    Definition Classes
    DiscreteDistr
  33. def unnormalizedProbabilityOf(x: Int): Double

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    Returns the probability of that draw up to a constant

    Returns the probability of that draw up to a constant

    Definition Classes
    DiscreteDistr
  34. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  35. final def wait(arg0: Long, arg1: Int): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  36. final def wait(arg0: Long): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  37. def withFilter(p: (Int) ⇒ Boolean): Rand[Int]

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    Definition Classes
    Rand

Inherited from Product

Inherited from Equals

Inherited from DiscreteDistr[Int]

Inherited from Rand[Int]

Inherited from Serializable

Inherited from Serializable

Inherited from Density[Int]

Inherited from AnyRef

Inherited from Any

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