class Beta extends ContinuousDistr[Double] with Moments[Double, Double] with HasCdf with HasInverseCdf

The Beta distribution, which is the conjugate prior for the Bernoulli distribution

Linear Supertypes
HasInverseCdf, HasCdf, Moments[Double, Double], ContinuousDistr[Double], Rand[Double], Serializable, Serializable, Density[Double], AnyRef, Any
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Inherited
  1. Beta
  2. HasInverseCdf
  3. HasCdf
  4. Moments
  5. ContinuousDistr
  6. Rand
  7. Serializable
  8. Serializable
  9. Density
  10. AnyRef
  11. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new Beta(a: Double, b: Double)(implicit rand: RandBasis = Rand)

    a

    the number of pseudo-observations for true

    b

    the number of pseudo-observations for false

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. def apply(x: Double): Double

    Returns the unnormalized value of the measure

    Returns the unnormalized value of the measure

    Definition Classes
    ContinuousDistr → Density
  5. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  6. def cdf(x: Double): Double
    Definition Classes
    Beta → HasCdf
  7. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  8. def condition(p: (Double) ⇒ Boolean): Rand[Double]
    Definition Classes
    Rand
  9. def draw(): Double

    Gets one sample from the distribution.

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

    Definition Classes
    Beta → Rand
  10. def drawOpt(): Option[Double]

    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
  11. def entropy: Double
    Definition Classes
    Beta → Moments
  12. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  13. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  14. def filter(p: (Double) ⇒ Boolean): Rand[Double]
    Definition Classes
    Rand
  15. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  16. def flatMap[E](f: (Double) ⇒ Rand[E]): Rand[E]

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

    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
  18. def get(): Double
    Definition Classes
    Rand
  19. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
  20. def hashCode(): Int
    Definition Classes
    AnyRef → Any
  21. def inverseCdf(p: Double): Double
    Definition Classes
    Beta → HasInverseCdf
  22. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  23. def logApply(x: Double): Double

    Returns the log unnormalized value of the measure

    Returns the log unnormalized value of the measure

    Definition Classes
    ContinuousDistr → Density
  24. lazy val logNormalizer: Double
    Definition Classes
    Beta → ContinuousDistr
  25. def logPdf(x: Double): Double
    Definition Classes
    ContinuousDistr
  26. def map[E](f: (Double) ⇒ E): Rand[E]

    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
  27. def mean: Double
    Definition Classes
    Beta → Moments
  28. def mode: Double
    Definition Classes
    Beta → Moments
  29. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  30. lazy val normalizer: Double
    Definition Classes
    ContinuousDistr
  31. final def notify(): Unit
    Definition Classes
    AnyRef
  32. final def notifyAll(): Unit
    Definition Classes
    AnyRef
  33. def pdf(x: Double): Double

    Returns the probability density function at that point.

    Returns the probability density function at that point.

    Definition Classes
    Beta → ContinuousDistr
  34. def probability(x: Double, y: Double): Double
    Definition Classes
    Beta → HasCdf
  35. def sample(n: Int): IndexedSeq[Double]

    Gets n samples from the distribution.

    Gets n samples from the distribution.

    Definition Classes
    Rand
  36. def sample(): Double

    Gets one sample from the distribution.

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

    Definition Classes
    Rand
  37. def samples: Iterator[Double]

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

    Return a vector of samples.

    Return a vector of samples.

    Definition Classes
    Rand
  39. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  40. def toString(): String
    Definition Classes
    AnyRef → Any
  41. def unnormalizedLogPdf(x: Double): Double
    Definition Classes
    Beta → ContinuousDistr
  42. def unnormalizedPdf(x: Double): Double

    Returns the probability density function up to a constant at that point.

    Returns the probability density function up to a constant at that point.

    Definition Classes
    ContinuousDistr
  43. def variance: Double
    Definition Classes
    Beta → Moments
  44. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  45. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  46. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  47. def withFilter(p: (Double) ⇒ Boolean): Rand[Double]
    Definition Classes
    Rand

Inherited from HasInverseCdf

Inherited from HasCdf

Inherited from Moments[Double, 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