ContinuousDistr

Abstract Value Members

abstract def draw(): T

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

Definition Classes
Rand
abstract def logNormalizer: Double
abstract def unnormalizedLogPdf(x: T): Double

Concrete Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def apply(x: T): Double

Returns the unnormalized value of the measure
Returns the unnormalized value of the measure

Definition Classes
ContinuousDistr → Density
final def asInstanceOf[T0]: T0

Definition Classes
Any
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
def condition(p: (T) ⇒ Boolean): Rand[T]

Definition Classes
Rand
def drawOpt(): Option[T]

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
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def filter(p: (T) ⇒ Boolean): Rand[T]

Definition Classes
Rand
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
def flatMap[E](f: (T) ⇒ 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
def foreach(f: (T) ⇒ 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
def get(): T

Definition Classes
Rand
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
def hashCode(): Int

Definition Classes
AnyRef → Any
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
def logApply(x: T): Double

Returns the log unnormalized value of the measure
Returns the log unnormalized value of the measure

Definition Classes
ContinuousDistr → Density
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.
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
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
lazy val normalizer: Double
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
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.
Gets n samples from the distribution.

Definition Classes
Rand
def sample(): T

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

Definition Classes
Rand
def samples: Iterator[T]

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
def samplesVector[U >: T](size: Int)(implicit m: ClassTag[U]): DenseVector[U]

Return a vector of samples.
Return a vector of samples.

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

Definition Classes
AnyRef
def toString(): String

Definition Classes
AnyRef → Any
def unnormalizedPdf(x: T): Double

Returns the probability density function up to a constant at that point.
final def wait(): Unit

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

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

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

Definition Classes
Rand

Related Doc: package distributions

trait ContinuousDistr[T] extends Density[T] with Rand[T]

Abstract Value Members

abstract def draw(): T

abstract def logNormalizer: Double

abstract def unnormalizedLogPdf(x: T): Double

Concrete Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

def apply(x: T): Double

final def asInstanceOf[T0]: T0

def clone(): AnyRef

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

def drawOpt(): Option[T]

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]

def foreach(f: (T) ⇒ Unit): Unit

def get(): T

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

def logApply(x: T): Double

def logPdf(x: T): Double

def map[E](f: (T) ⇒ E): Rand[E]

final def ne(arg0: AnyRef): Boolean

lazy val normalizer: Double

final def notify(): Unit

final def notifyAll(): Unit

def pdf(x: T): Double

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

def sample(): T

def samples: Iterator[T]

def samplesVector[U >: T](size: Int)(implicit m: ClassTag[U]): DenseVector[U]

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

def toString(): String

def unnormalizedPdf(x: T): Double

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]

Inherited from Rand[T]

Inherited from Serializable

Inherited from Serializable

Inherited from Density[T]

Inherited from AnyRef

Inherited from Any

Ungrouped