ArbitraryMetropolisHastings

Instance Constructors

new ArbitraryMetropolisHastings(logLikelihood: (T) ⇒ Double, proposal: (T) ⇒ Rand[T], logProposalDensity: (T, T) ⇒ Double, init: T, burnIn: Long = 0, dropCount: Int = 0)(implicit rand: RandBasis = Rand)

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 aboveOneCount: Long

Definition Classes
BaseMetropolisHastings → TracksStatistics
def aboveOneFrac: Double

Definition Classes
TracksStatistics
def acceptanceCount: Long

Definition Classes
BaseMetropolisHastings → TracksStatistics
final def asInstanceOf[T0]: T0

Definition Classes
Any
val burnIn: Long
def clone(): AnyRef

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

Definition Classes
Rand
def draw(): T

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

Definition Classes
BaseMetropolisHastings → 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
val dropCount: Int
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
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
val init: T
final def isInstanceOf[T0]: Boolean

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

Definition Classes
MetropolisHastings
def likelihoodRatio(start: T, end: T): Double

Definition Classes
MetropolisHastings
def logLikelihood(x: T): Double

Definition Classes
BaseMetropolisHastings → MetropolisHastings
val logLikelihood: (T) ⇒ Double
val logProposalDensity: (T, T) ⇒ Double
def logTransitionProbability(start: T, end: T): Double

Definition Classes
ArbitraryMetropolisHastings → MetropolisHastings
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
def nextDouble: Double

Attributes
protected
Definition Classes
MetropolisHastings
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
def observe(x: T): ArbitraryMetropolisHastings[T]

Force the "next" draw to be x, and return a new process.
Force the "next" draw to be x, and return a new process.

Definition Classes
ArbitraryMetropolisHastings → Process
val proposal: (T) ⇒ Rand[T]
def proposalDraw(x: T): T

Definition Classes
ArbitraryMetropolisHastings → MetropolisHastings
implicit val rand: RandBasis

Definition Classes
BaseMetropolisHastings → MetropolisHastings
def rejectionCount: Long

Definition Classes
TracksStatistics
def rejectionFrac: Double

Definition Classes
TracksStatistics
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
def step(): (T, Process[T])

Draw a sample and the next step of the process along with it.
Draw a sample and the next step of the process along with it.

Definition Classes
Process
def steps: Iterator[T]

Returns an Iterator that automatically moves the Process along as next is called
Returns an Iterator that automatically moves the Process along as next is called

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

Definition Classes
AnyRef
def total: Long

Definition Classes
BaseMetropolisHastings → TracksStatistics
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 mcmc

case class ArbitraryMetropolisHastings[T](logLikelihood: (T) ⇒ Double, proposal: (T) ⇒ Rand[T], logProposalDensity: (T, T) ⇒ Double, init: T, burnIn: Long = 0, dropCount: Int = 0)(implicit rand: RandBasis = Rand) extends BaseMetropolisHastings[T] with Product with Serializable

Instance Constructors

new ArbitraryMetropolisHastings(logLikelihood: (T) ⇒ Double, proposal: (T) ⇒ Rand[T], logProposalDensity: (T, T) ⇒ Double, init: T, burnIn: Long = 0, dropCount: Int = 0)(implicit rand: RandBasis = Rand)

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

def aboveOneCount: Long

def aboveOneFrac: Double

def acceptanceCount: Long

final def asInstanceOf[T0]: T0

val burnIn: Long

def clone(): AnyRef

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

def draw(): T

def drawOpt(): Option[T]

val dropCount: Int

final def eq(arg0: AnyRef): 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[_]

val init: T

final def isInstanceOf[T0]: Boolean

def likelihood(x: T): Double

def likelihoodRatio(start: T, end: T): Double

def logLikelihood(x: T): Double

val logLikelihood: (T) ⇒ Double

val logProposalDensity: (T, T) ⇒ Double

def logTransitionProbability(start: T, end: T): Double

def map[E](f: (T) ⇒ E): Rand[E]

final def ne(arg0: AnyRef): Boolean

def nextDouble: Double

final def notify(): Unit

final def notifyAll(): Unit

def observe(x: T): ArbitraryMetropolisHastings[T]

val proposal: (T) ⇒ Rand[T]

def proposalDraw(x: T): T

implicit val rand: RandBasis

def rejectionCount: Long

def rejectionFrac: 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]

def step(): (T, Process[T])

def steps: Iterator[T]

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

def total: Long

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 Product

Inherited from Equals

Inherited from BaseMetropolisHastings[T]

Inherited from TracksStatistics

Inherited from Process[T]

Inherited from MetropolisHastings[T]

Inherited from Rand[T]

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped