ProbQueryGibbs

abstract def createBlocks(): List[Block]

Method to create a blocking scheme given information about the model and factors.

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

var active: Boolean

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

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

final def asInstanceOf[T0]: T0

var blockSamplers: List[BlockSampler]

val blockToSampler: BlockSamplerCreator

val burnIn: Int

Number of samples to throw away initially.

def chainMapper(chain: Chain[_, _]): Set[Variable[_]]

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 computeProjection[T](target: Element[T]): List[(T, Double)]

val currentSamples: Map[Variable[_], Int]

The most recent set of samples, used for sampling variables conditioned on the values of other variables.

val dependentAlgorithm: (Universe, List[NamedEvidence[_]]) ⇒ () ⇒ Double

The algorithm to compute probability of specified evidence in a dependent universe.

val dependentUniverses: List[(Universe, List[NamedEvidence[_]])]

A list of universes that depend on this universe such that evidence on those universes should be taken into account in this universe.

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 doProjection[T](target: Element[T]): List[(T, Double)]

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.

var factors: List[Factor[Double]]

List of all factors.

def finalize(): Unit

final def getClass(): Class[_]

def getFactors(neededElements: List[Element[_]], targetElements: List[Element[_]], upperBounds: Boolean = false): List[Factor[Double]]

All implementations of factored algorithms must specify a way to get the factors from the given universe and dependent universes.

def getNeededElements(starterElements: List[Element[_]], depth: Int): (List[Element[_]], Boolean)

Get the elements that are needed by the query target variables and the evidence variables.

def getSampleCount: Int

Number of samples taken

def getTotalWeight: Double

Total weight of samples taken, in log space

def hashCode(): Int

def initUpdates(): Unit

def initialize(): Unit

Called when the algorithm is started before running any steps.

val interval: Int

Iterations thrown away between samples.

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

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

final def notify(): Unit

final def notifyAll(): Unit

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.

def sample(): (Boolean, Sample)

Produce a single sample.

def sampleAllBlocks(): Unit

var sampleCount: Int

val semiring: LogSumProductSemiring

Semiring for use in factors.

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

val targetElements: List[Element[_]]

Elements whose samples will be recorded at each iteration.

def toString(): String

val universe: Universe

The universe in which this Gibbs sampler is to be applied.

def update(): Unit

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

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

var variables: Set[Variable[_]]

Variables to sample at each time step.

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