IIDContinuousRVDistr

abstract val baseRandomVariable: R

abstract val num: Int

final def !=(arg0: Any): Boolean

final def ##(): Int

def :*[Domain1, Dist1 <: Density[Domain1] with Rand[Domain1]](other: RandomVarWithDistr[Domain1, Dist1]): RandomVarWithDistr[(Stream[D], Domain1), GenericDistribution[(Stream[D], Domain1)]]

Cartesian product with another random variables which has a defined probability distribution.

def :*[Domain1](other: RandomVariable[Domain1]): RandomVariable[(Stream[D], Domain1)]

Outputs the cartesian product between two random variables.

final def ==(arg0: Any): Boolean

def >[OtherDomain](transformation: DataPipe[Stream[D], OtherDomain]): MeasurableFunction[Stream[D], OtherDomain, RandomVariable[Stream[D]]]

Transform the current random variable on Domain to a morphed random variable on OtherDomain

final def asInstanceOf[T0]: T0

def clone(): AnyRef

def draw: Stream[D]

Alias for sample.run()

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

def iid(n: Int): RandomVariable[Stream[Stream[D]]] with IIDRandomVariable[Stream[D], RandomVarWithDistr[Stream[D], Density[Stream[D]] with Rand[Stream[D]]]]

Create an iid random variable from the current (this)

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

val sample: DataPipe[Unit, Stream[D]]

Generate a sample from the random variable

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

def toString(): String

val underlyingDist: AbstractContinuousDistr[Stream[D]]

The actual probability density function is represented as a breeze Density object.

final def wait(): Unit

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

final def wait(arg0: Long): Unit