t

# ProbEvidenceBeliefPropagation 

### Companion object ProbEvidenceBeliefPropagation

#### trait ProbEvidenceBeliefPropagation extends ProbabilisticBeliefPropagation with ProbEvidenceAlgorithm

Ordering
1. Alphabetic
2. By Inheritance
Inherited
1. ProbEvidenceBeliefPropagation
2. ProbEvidenceAlgorithm
3. ProbabilisticBeliefPropagation
4. BeliefPropagation
5. FactoredAlgorithm
6. Algorithm
7. AnyRef
8. Any
1. Hide All
2. Show All
Visibility
1. Public
2. All

### Abstract Value Members

The algorithm used to compute the probability of additional evidence, as created by probAdditionalEvidence.

The algorithm used to compute the probability of additional evidence, as created by probAdditionalEvidence. This algorithm can be different to the one defined in this class. (For example, a one-time algorithm can use an anytime algorithm for additional evidence.)

Definition Classes
ProbEvidenceAlgorithm
2. 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
3. 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
4. abstract def doKill(): Unit
Attributes
protected[com.cra.figaro.algorithm]
Definition Classes
Algorithm
5. abstract def doResume(): Unit
Attributes
protected[com.cra.figaro.algorithm]
Definition Classes
Algorithm
6. abstract def doStart(): Unit
Attributes
protected[com.cra.figaro.algorithm]
Definition Classes
Algorithm
7. abstract def doStop(): Unit
Attributes
protected[com.cra.figaro.algorithm]
Definition Classes
Algorithm
8. 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
9. 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
10. abstract val universe
Definition Classes
ProbEvidenceAlgorithm

### 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

Removes the evidence provided in the constructor from the universe.

Removes the evidence provided in the constructor from the universe.

Definition Classes
ProbEvidenceAlgorithmAlgorithm
8. def clone(): AnyRef
Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
9. def computedResult(): Double

Compute the evidence of the model.

Compute the evidence of the model. Returns the probability of evidence on the model. This assumes that BP has already been run on this algorithm instance.

Definition Classes
ProbEvidenceBeliefPropagationProbEvidenceAlgorithm
10. def convertFactors(factors: List[Factor[Double]]): List[Factor[Double]]
Attributes
protected
Definition Classes
ProbabilisticBeliefPropagation
11. 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
12. val denominator: Double
Definition Classes
ProbEvidenceAlgorithm
13. final def eq(arg0: AnyRef): Boolean
Definition Classes
AnyRef
14. def equals(arg0: Any): Boolean
Definition Classes
AnyRef → Any
15. val evidence: List[NamedEvidence[_]]
Definition Classes
ProbEvidenceAlgorithm
16. val factorGraph: FactorGraph[Double]
Attributes
protected[com.cra.figaro]
Definition Classes
BeliefPropagation
17. def factorToBeliefs[T](factor: Factor[Double]): List[Tuple2[Double, _]]
Attributes
protected[com.cra.figaro]
Definition Classes
ProbabilisticBeliefPropagation
18. def finalize(): Unit
Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
19. def findNodeForElement[T](target: Element[T]): Node
Attributes
protected[com.cra.figaro]
Definition Classes
ProbabilisticBeliefPropagation
20. 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]
Definition Classes
ProbabilisticBeliefPropagation
21. final def getClass(): Class[_]
Definition Classes
AnyRef → Any
22. 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
23. def getFinalFactorForElement[T](target: Element[T]): Factor[Double]

Get the final factor for an element.

Get the final factor for an element.

Definition Classes
ProbabilisticBeliefPropagation
24. 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
25. def getNewMessageFactorToVar(fn: FactorNode, vn: VariableNode): Factor[Double]
Attributes
protected
Definition Classes
BeliefPropagation
26. def getNewMessageVarToFactor(vn: VariableNode, fn: FactorNode): Factor[Double]
Attributes
protected
Definition Classes
BeliefPropagation
27. def hashCode(): Int
Definition Classes
AnyRef → Any
28. def initialize(): Unit

Since probability of evidence algorithms introduce additional evidence (namely, their evidence argument), into an existing universe, a mechanism must be provided for introducing the evidence when the algorithm begins and cleaning it up at the end.

Since probability of evidence algorithms introduce additional evidence (namely, their evidence argument), into an existing universe, a mechanism must be provided for introducing the evidence when the algorithm begins and cleaning it up at the end. This is achieved with the initialize method, called when the algorithm starts, and the cleanUp method, called when the algorithm is killed.

Definition Classes
ProbEvidenceAlgorithmAlgorithm
29. def isActive: Boolean
Definition Classes
Algorithm
30. final def isInstanceOf[T0]: Boolean
Definition Classes
Any
31. 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
32. def logProbEvidence: Double

The computed log probability of evidence.

The computed log probability of evidence.

Definition Classes
ProbEvidenceAlgorithm
33. 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
34. final def ne(arg0: AnyRef): Boolean
Definition Classes
AnyRef
35. def newMessage(source: Node, target: Node): Factor[Double]
Attributes
protected[com.cra.figaro]
Definition Classes
ProbabilisticBeliefPropagationBeliefPropagation
36. def normalize(factor: Factor[Double]): Factor[Double]

Normalize a factor.

Normalize a factor.

Definition Classes
ProbabilisticBeliefPropagation
37. final def notify(): Unit
Definition Classes
AnyRef
38. final def notifyAll(): Unit
Definition Classes
AnyRef

Returns an algorithm to compute the probability of the additional evidence provided.

Returns an algorithm to compute the probability of the additional evidence provided.

Definition Classes
ProbEvidenceAlgorithm
40. def probEvidence: Double

The computed probability of evidence.

The computed probability of evidence.

Definition Classes
ProbEvidenceAlgorithm
41. 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
42. 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
43. 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
44. 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
45. 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
46. final def synchronized[T0](arg0: ⇒ T0): T0
Definition Classes
AnyRef
47. def toString(): String
Definition Classes
AnyRef → Any
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( ... )