Importance

abstract def doDistribution[T](target: Element[T]): Stream[(Double, T)]

abstract def doExpectation[T](target: Element[T], function: (T) ⇒ Double): Double

abstract def doKill(): Unit

abstract def doProbability[T](target: Element[T], predicate: (T) ⇒ Boolean): Double

abstract def doResume(): Unit

abstract def doStart(): Unit

abstract def doStop(): Unit

final def !=(arg0: AnyRef): Boolean

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: AnyRef): Boolean

final def ==(arg0: Any): Boolean

var active: Boolean

var allWeightsSeen: List[WeightSeen[_]]

final def asInstanceOf[T0]: T0

def cleanUp(): Unit

Called when the algorithm is killed.

def clone(): AnyRef

def computeDistribution[T](target: Element[T]): Stream[(Double, T)]

Return an estimate of the expectation of the function under the marginal probability distribution of the target.

def computeExpectation[T](target: Element[T], function: (T) ⇒ Double): Double

Return an estimate of the expectation of the function under the marginal probability distribution of the target.

def computeProbability[T](target: Element[T], predicate: (T) ⇒ Boolean): Double

Return an estimate of the probability of the predicate under the marginal probability distribution of the target.

def distribution[T](target: Element[T]): Stream[(Double, T)]

Return an estimate of the marginal probability distribution over the target that lists each element with its probability.

def doSample(): Unit

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def expectation[T](target: Element[T], function: (T) ⇒ Double): Double

Return an estimate of the expectation of the function under the marginal probability distribution of the target.

def finalize(): Unit

final def getClass(): Class[_]

def getTotalWeight: Double

Total weight of samples taken.

def hashCode(): Int

def initialize(): Unit

Called when the algorithm is started before running any steps.

def isActive: Boolean

final def isInstanceOf[T0]: Boolean

def kill(): Unit

Kill the algorithm so that it is inactive.

def mean(target: Element[Double]): Double

Return the mean of the probability density function for the given continuous element

final def ne(arg0: AnyRef): Boolean

def newWeightSeen[T](target: Element[T]): (Element[T], Map[T, Double])

final def notify(): Unit

final def notifyAll(): Unit

def posteriorElement[T](target: Element[T], universe: Universe = Universe.universe): Element[T]

Return an element representing the posterior probability distribution of the given element

def probability[T](target: Element[T], value: T): Double

Return an estimate of the probability that the target produces the value.

def probability[T](target: Element[T], predicate: (T) ⇒ Boolean): Double

Return an estimate of the probability of the predicate under the marginal probability distribution of the target.

lazy val queryTargets: List[Element[_]]

def resetCounts(): Unit

def resume(): Unit

Resume the computation of the algorithm, if it has been stopped.

final def sample(): (Double, Map[Element[_], Any])

Produce a single sample.

def start(): Unit

Start the algorithm and make it active.

def stop(): Unit

Stop the algorithm from computing.

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

def toString(): String

var totalWeight: Double

def update(): Unit

def updateWeightSeenForTarget[T](sample: (Double, Map[Element[_], Any]), weightSeen: (Element[T], Map[T, Double])): Unit

def updateWeightSeenWithValue[T](value: T, weight: Double, weightSeen: (Element[T], Map[T, Double])): Unit

def variance(target: Element[Double]): Double

Return the variance of the probability density function for the given continuous element

final def wait(): Unit

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

final def wait(arg0: Long): Unit