ContinuousDistr

abstract def draw(): T

Gets one sample from the distribution.

abstract def logNormalizer: Double

abstract def unnormalizedLogPdf(x: T): Double

final def !=(arg0: AnyRef): Boolean

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: AnyRef): Boolean

final def ==(arg0: Any): Boolean

def apply(x: T): Double

Returns the unnormalized value of the measure

final def asInstanceOf[T0]: T0

def clone(): AnyRef

def condition(p: (T) ⇒ Boolean): Rand[T]

def drawOpt(): Option[T]

Overridden by filter/map/flatmap for monadic invocations.

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def filter(p: (T) ⇒ Boolean): Rand[T]

def finalize(): Unit

def flatMap[E](f: (T) ⇒ Rand[E]): Rand[E]

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

def foreach(f: (T) ⇒ Unit): Unit

Samples one element and qpplies the provided function to it.

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

def get(): T

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

def logApply(x: T): Double

Returns the log unnormalized value of the measure

def logPdf(x: T): Double

def map[E](f: (T) ⇒ E): Rand[E]

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

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def pdf(x: T): Double

Returns the probability density function at that point.

def sample(n: Int): IndexedSeq[T]

Gets n samples from the distribution.

def sample(): T

Gets one sample from the distribution.

def samples: Iterator[T]

An infinitely long iterator that samples repeatedly from the Rand

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

def toString(): String

def unnormalizedPdf(x: T): Double

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

final def wait(): Unit

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

final def wait(arg0: Long): Unit

def withFilter(p: (T) ⇒ Boolean): Rand[T]