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# ProbabilisticGibbs 

#### trait ProbabilisticGibbs extends BaseUnweightedSampler with Gibbs[Double]

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Inherited
1. ProbabilisticGibbs
2. Gibbs
3. FactoredAlgorithm
4. BaseUnweightedSampler
5. Sampler
6. Algorithm
7. AnyRef
8. Any
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### Type Members

1. type LastUpdate[T] = (T, Int)
Attributes
protected
Definition Classes
BaseUnweightedSampler
2. type Sample = Map[Element[_], Any]

A sample is a map from elements to their values.

A sample is a map from elements to their values.

Definition Classes
BaseUnweightedSampler
3. class
4. type TimesSeen[T] = Map[T, Int]
Attributes
protected
Definition Classes
BaseUnweightedSampler

### Abstract Value Members

1. abstract def burnIn(): Int

Number of samples to throw away initially.

Number of samples to throw away initially.

Definition Classes
Gibbs
2. abstract def createBlocks(): List[Block]

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

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

Definition Classes
Gibbs
3. abstract val dependentAlgorithm: (Universe, List[NamedEvidence[_]]) ⇒ () ⇒ Double

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

The algorithm to compute probability of specified evidence in a dependent universe. We use () => Double to represent this algorithm instead of an instance of ProbEvidenceAlgorithm. Typical usage is to return the result of ProbEvidenceAlgorithm.computeProbEvidence when invoked.

Definition Classes
FactoredAlgorithm
4. abstract 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.

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

Definition Classes
FactoredAlgorithm
5. abstract def doKill(): Unit
Attributes
protected[com.cra.figaro.algorithm]
Definition Classes
Algorithm
6. abstract def doResume(): Unit
Attributes
protected[com.cra.figaro.algorithm]
Definition Classes
Algorithm
7. abstract def doStart(): Unit
Attributes
protected[com.cra.figaro.algorithm]
Definition Classes
Algorithm
8. abstract def doStop(): Unit
Attributes
protected[com.cra.figaro.algorithm]
Definition Classes
Algorithm
9. abstract def interval(): Int

Iterations thrown away between samples.

Iterations thrown away between samples.

Definition Classes
Gibbs
10. abstract val targetElements: List[Element[_]]

Elements whose samples will be recorded at each iteration.

Elements whose samples will be recorded at each iteration.

Definition Classes
Gibbs

### Concrete Value Members

1. final def !=(arg0: Any): Boolean
Definition Classes
AnyRef → Any
2. final def ##(): Int
Definition Classes
AnyRef → Any
3. final def ==(arg0: Any): Boolean
Definition Classes
AnyRef → Any
4. val active: Boolean
Attributes
protected
Definition Classes
Algorithm
Attributes
protected
Definition Classes
BaseUnweightedSampler
6. var allTimesSeen: Map[Element[_], TimesSeen[_]]
Attributes
protected
Definition Classes
BaseUnweightedSampler
7. final def asInstanceOf[T0]: T0
Definition Classes
Any
8. val blockSamplers: List[BlockSampler]
Attributes
protected
9. def cleanUp(): Unit

Called when the algorithm is killed.

Called when the algorithm is killed. By default, does nothing. Can be overridden.

Definition Classes
Algorithm
10. def clone(): AnyRef
Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
11. val currentSamples: Map[Variable[_], Int]

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

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

Definition Classes
Gibbs
12. def doSample(): Unit
Attributes
protected
Definition Classes
ProbabilisticGibbsBaseUnweightedSamplerSampler
13. final def eq(arg0: AnyRef): Boolean
Definition Classes
AnyRef
14. def equals(arg0: Any): Boolean
Definition Classes
AnyRef → Any
15. val factors: List[Factor[Double]]

List of all factors.

List of all factors.

Definition Classes
Gibbs
16. def finalize(): Unit
Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
17. final def getClass(): Class[_]
Definition Classes
AnyRef → Any
18. 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.

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

Definition Classes
ProbabilisticGibbsFactoredAlgorithm
19. def getNeededElements(starterElements: List[Element[_]], depth: Int, parameterized: Boolean = false): (List[Element[_]], Boolean)

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

Get the elements that are needed by the query target variables and the evidence variables. Also compute the values of those variables to the given depth. Only get factors for elements that are actually used by the target variables. This is more efficient. Also, it avoids problems when values of unused elements have not been computed.

In addition to getting all the needed elements, it determines if any of the conditioned, constrained, or dependent universe parent elements has * in its range. If any of these elements has * in its range, the lower and upper bounds of factors will be different, so we need to compute both. If they don't, we don't need to compute bounds.

Definition Classes
FactoredAlgorithm
20. def getSampleCount: Int

Number of samples taken

Number of samples taken

Definition Classes
BaseUnweightedSampler
21. def hashCode(): Int
Definition Classes
AnyRef → Any
Attributes
protected
Definition Classes
BaseUnweightedSampler
23. def initialize(): Unit

Called when the algorithm is started before running any steps.

Called when the algorithm is started before running any steps. By default, does nothing. Can be overridden.

Definition Classes
Algorithm
24. def isActive: Boolean
Definition Classes
Algorithm
25. final def isInstanceOf[T0]: Boolean
Definition Classes
Any
26. def kill(): Unit

Kill the algorithm so that it is inactive.

Kill the algorithm so that it is inactive. It will no longer be able to provide answers.Throws AlgorithmInactiveException if the algorithm is not active.

Definition Classes
Algorithm
27. final def ne(arg0: AnyRef): Boolean
Definition Classes
AnyRef
28. def newLastUpdate[T](target: Element[T]): LastUpdate[T]
Attributes
protected
Definition Classes
BaseUnweightedSampler
29. def newTimesSeen[T](target: Element[T]): TimesSeen[T]
Attributes
protected
Definition Classes
BaseUnweightedSampler
30. final def notify(): Unit
Definition Classes
AnyRef
31. final def notifyAll(): Unit
Definition Classes
AnyRef
32. lazy val queryTargets: List[Element[_]]
Definition Classes
BaseUnweightedSampler
33. def resetCounts(): Unit
Attributes
protected
Definition Classes
BaseUnweightedSamplerSampler
34. def resume(): Unit

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

Resume the computation of the algorithm, if it has been stopped. Throws AlgorithmInactiveException if the algorithm is not active.

Definition Classes
Algorithm
35. def sample(): (Boolean, Sample)

Produce a single sample.

Produce a single sample.

Definition Classes
ProbabilisticGibbsBaseUnweightedSampler
36. def sampleAllBlocks(): Unit
37. var sampleCount: Int
Attributes
protected
Definition Classes
BaseUnweightedSampler
38. val semiring

Semiring for use in factors.

Semiring for use in factors.

Definition Classes
ProbabilisticGibbsGibbsFactoredAlgorithm
39. def start(): Unit

Start the algorithm and make it active.

Start the algorithm and make it active. After it returns, the algorithm must be ready to provide answers. Throws AlgorithmActiveException if the algorithm is already active.

Definition Classes
Algorithm
40. def stop(): Unit

Stop the algorithm from computing.

Stop the algorithm from computing. The algorithm is still ready to provide answers after it returns. Throws AlgorithmInactiveException if the algorithm is not active.

Definition Classes
Algorithm
41. final def synchronized[T0](arg0: ⇒ T0): T0
Definition Classes
AnyRef
42. def toString(): String
Definition Classes
AnyRef → Any
43. val universe
Definition Classes
BaseUnweightedSampler
44. def update(): Unit
Attributes
protected
Definition Classes
BaseUnweightedSamplerSampler
45. def updateTimesSeenForTarget[T](elem: Element[T], newValue: T): Unit
Attributes
protected
Definition Classes
BaseUnweightedSampler
46. def updateTimesSeenWithValue[T](value: T, timesSeen: TimesSeen[T], seen: Int): Unit
Attributes
protected
Definition Classes
BaseUnweightedSampler
47. val variables: Set[Variable[_]]

Variables to sample at each time step.

Variables to sample at each time step.

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