Beta

case class Beta(a: Double, b: Double)(implicit rand: RandBasis) extends ContinuousDistr[Double] with Moments[Double, Double] with HasCdf with HasInverseCdf

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

Value parameters:
a

the number of pseudo-observations for true

b

the number of pseudo-observations for false

Companion:
object
trait Product
trait Equals
trait HasCdf
trait Rand[Double]
trait Serializable
class Object
trait Matchable
class Any

Value members

Concrete methods

override def cdf(x: Double): Double
Definition Classes
override def draw(): Double
Definition Classes
override def inverseCdf(p: Double): Double
Definition Classes
override def pdf(x: Double): Double
Definition Classes
override def probability(x: Double, y: Double): Double
Definition Classes
override def unnormalizedLogPdf(x: Double): Double
Definition Classes

Inherited methods

Inherited from:
ContinuousDistr
Inherited from:
Rand

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

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

Inherited from:
Rand
Inherited from:
Rand
def flatMap[E](f: Double => Rand[E]): Rand[E]

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

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

Value parameters:
f

the transform to apply to the sampled value.

Inherited from:
Rand
def foreach(f: Double => Unit): Unit

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

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) } 
Value parameters:
f

the function to be applied

Inherited from:
Rand
def get(): Double
Inherited from:
Rand
override def logApply(x: Double): Double
Definition Classes
Inherited from:
ContinuousDistr
Inherited from:
ContinuousDistr
def map[E](f: Double => E): Rand[E]

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 2x

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 2x

Value parameters:
f

the transform to apply to the sampled value.

Inherited from:
Rand
Inherited from:
Product

Gets n samples from the distribution.

Gets n samples from the distribution.

Inherited from:
Rand
def sample(): Double

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

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

Inherited from:
Rand

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

Inherited from:
Rand
def samplesVector[U >: Double](size: Int)(implicit m: ClassTag[U]): DenseVector[U]

Return a vector of samples.

Return a vector of samples.

Inherited from:
Rand

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

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

Inherited from:
ContinuousDistr
Inherited from:
Rand

Concrete fields

Inherited fields

lazy val normalizer: Double
Inherited from:
ContinuousDistr