MetropolisHastings

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: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

def acceptRejectRatio: Double

Get the acceptance ratio for the sampler.

var active: Boolean

var allLastUpdates: Map[Element[_], LastUpdate[_]]

var allTimesSeen: Map[Element[_], TimesSeen[_]]

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 marginal probability distribution over the target that lists each element with its probability.

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.

var debug: Boolean

Set this flag to true to obtain debugging information.

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 doInitialize(): Unit

final 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 getSampleCount: Int

Number of samples taken.

def hashCode(): Int

def initUpdates(): Unit

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 mhStep(): State

final def ne(arg0: AnyRef): Boolean

def newLastUpdate[T](target: Element[T]): LastUpdate[T]

def newTimesSeen[T](target: Element[T]): TimesSeen[T]

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(): (Boolean, Sample)

Produce a single sample.

var sampleCount: Int

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 test(numSamples: Int, predicates: Seq[Predicate[_]], elementsToTrack: Seq[Element[_]]): (Double, Map[Predicate[_], Double], Map[Element[_], Double])

Test Metropolis-Hastings by repeatedly running a single step from the same initial state.

def toString(): String

val universe: Universe

def update(): Unit

def updateTimesSeenForTarget[T](elem: Element[T], newValue: T): Unit

def updateTimesSeenWithValue[T](value: T, timesSeen: TimesSeen[T], seen: Int): 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