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# ProbabilisticBeliefPropagation 

#### trait ProbabilisticBeliefPropagation extends BeliefPropagation[Double]

Trait for probabilistic BP algorithms.

Linear Supertypes
BeliefPropagation[Double], FactoredAlgorithm[Double], Algorithm, AnyRef, Any
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Inherited
1. ProbabilisticBeliefPropagation
2. BeliefPropagation
3. FactoredAlgorithm
4. Algorithm
5. AnyRef
6. Any
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Visibility
1. Public
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### Abstract Value Members

1. 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
2. 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
3. abstract def doKill(): Unit
Attributes
protected[com.cra.figaro.algorithm]
Definition Classes
Algorithm
4. abstract def doResume(): Unit
Attributes
protected[com.cra.figaro.algorithm]
Definition Classes
Algorithm
5. abstract def doStart(): Unit
Attributes
protected[com.cra.figaro.algorithm]
Definition Classes
Algorithm
6. abstract def doStop(): Unit
Attributes
protected[com.cra.figaro.algorithm]
Definition Classes
Algorithm
7. abstract val semiring: LogConvertibleSemiRing[Double]

Since BP uses division to compute messages, the semiring has to have a division function defined and must be log convertable.

Since BP uses division to compute messages, the semiring has to have a division function defined and must be log convertable. Note that BP operates in log space and any semiring must be log convertible If you define a non-log semiring, it will automatically convert, and convert it back to normal space at the end If you define a log semiring, it won't convert to log or convert from log. In other words, it outputs the answer in the space specified by the semiring

Definition Classes
BeliefPropagationFactoredAlgorithm
8. abstract val targetElements: List[Element[_]]

Target elements that should not be eliminated but should be available for querying.

Target elements that should not be eliminated but should be available for querying.

Definition Classes
BeliefPropagation
9. abstract val universe

The universe on which this belief propagation algorithm should be applied.

The universe on which this belief propagation algorithm should be applied.

Definition Classes
BeliefPropagationFactoredAlgorithm

### 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
5. final def asInstanceOf[T0]: T0
Definition Classes
Any
6. def belief(source: Node): Factor[Double]

Returns the product of all messages from a source node's neighbors to itself.

Returns the product of all messages from a source node's neighbors to itself.

Definition Classes
BeliefPropagation
7. 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
8. def clone(): AnyRef
Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
9. def convertFactors(factors: List[Factor[Double]]): List[Factor[Double]]
Attributes
protected
10. val debug: Boolean

By default, implementations that inherit this trait have no debug information.

By default, implementations that inherit this trait have no debug information. Override this if you want a debugging option.

Definition Classes
BeliefPropagation
11. final def eq(arg0: AnyRef): Boolean
Definition Classes
AnyRef
12. def equals(arg0: Any): Boolean
Definition Classes
AnyRef → Any
13. val factorGraph: FactorGraph[Double]
Attributes
protected[com.cra.figaro]
Definition Classes
BeliefPropagation
14. def factorToBeliefs[T](factor: Factor[Double]): List[Tuple2[Double, _]]
Attributes
protected[com.cra.figaro]
15. def finalize(): Unit
Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
16. def findNodeForElement[T](target: Element[T]): Node
Attributes
protected[com.cra.figaro]
17. def getBeliefsForElement[T](target: Element[T]): List[(Double, T)]

Get the belief for an element.

Get the belief for an element.

Attributes
protected[com.cra.figaro]
18. final def getClass(): Class[_]
Definition Classes
AnyRef → Any
19. def getFactors(neededElements: List[Element[_]], targetElements: List[Element[_]], upperBounds: Boolean = false): List[Factor[Double]]

Returns the factors needed for BP.

Returns the factors needed for BP. Since BP operates on a complete factor graph, factors are created for all elements in the universe.

Definition Classes
ProbabilisticBeliefPropagationFactoredAlgorithm
20. def getFinalFactorForElement[T](target: Element[T]): Factor[Double]

Get the final factor for an element.

21. 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
22. def getNewMessageFactorToVar(fn: FactorNode, vn: VariableNode): Factor[Double]
Attributes
protected
Definition Classes
BeliefPropagation
23. def getNewMessageVarToFactor(vn: VariableNode, fn: FactorNode): Factor[Double]
Attributes
protected
Definition Classes
BeliefPropagation
24. def hashCode(): Int
Definition Classes
AnyRef → Any
25. 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
BeliefPropagationAlgorithm
26. def isActive: Boolean
Definition Classes
Algorithm
27. final def isInstanceOf[T0]: Boolean
Definition Classes
Any
28. 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
29. def logSpaceSemiring(): LogConvertibleSemiRing[Double]

Returns the log space version of the semiring (or the semiring if already in log space)

Returns the log space version of the semiring (or the semiring if already in log space)

Attributes
protected
Definition Classes
BeliefPropagation
30. final def ne(arg0: AnyRef): Boolean
Definition Classes
AnyRef
31. def newMessage(source: Node, target: Node): Factor[Double]
Attributes
protected[com.cra.figaro]
Definition Classes
ProbabilisticBeliefPropagationBeliefPropagation
32. def normalize(factor: Factor[Double]): Factor[Double]

Normalize a factor.

33. final def notify(): Unit
Definition Classes
AnyRef
34. final def notifyAll(): Unit
Definition Classes
AnyRef
35. 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
36. def runStep(): Unit

Runs this belief propagation algorithm for one iteration.

Runs this belief propagation algorithm for one iteration. An iteration consists of each node of the factor graph sending a message to each of its neighbors.

Definition Classes
BeliefPropagation
37. 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
38. def starterElements: List[Element[_]]

Elements towards which queries are directed.

Elements towards which queries are directed. By default, these are the target elements. This is overridden by DecisionVariableElimination, where it also includes utility variables.

Definition Classes
BeliefPropagation
39. 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
40. final def synchronized[T0](arg0: ⇒ T0): T0
Definition Classes
AnyRef
41. def toString(): String
Definition Classes
AnyRef → Any
42. final def wait(): Unit
Definition Classes
AnyRef
Annotations
@throws( ... )
43. final def wait(arg0: Long, arg1: Int): Unit
Definition Classes
AnyRef
Annotations
@throws( ... )
44. final def wait(arg0: Long): Unit
Definition Classes
AnyRef
Annotations
@throws( ... )