t

# ProbEvidenceAlgorithm 

#### trait ProbEvidenceAlgorithm extends Algorithm

Algorithms that compute probability of evidence. The evidence is specified as a list of named Evidence items. In addition to the universe, the ProbEvidenceAlgorithm takes two optional arguments. The evidence, which defaults to empty, and the denominator, which defaults to 1. The evidence is a list of specific evidence items associated with references. When started, the algorithm computes the probability of the named evidence, in addition to the conditions and constraints in the model, divided by the denominator (partition function). In a typical use case, one might want to compute the probability of the named evidence, taking the conditions and constraints as a given part of the model. To achieve this, you would create a ProbEvidenceAlgorithm with no evidence to compute the probability of the conditions and constraints. This probability becomes the denominator in a subsequent algorithm that takes the named evidence whose probability you want to compute. Several shortcut ways of achieving this are provided.

Linear Supertypes
Algorithm, AnyRef, Any
Ordering
1. Alphabetic
2. By Inheritance
Inherited
1. ProbEvidenceAlgorithm
2. Algorithm
3. AnyRef
4. 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.)

2. abstract def computedResult: Double
Attributes
protected
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 universe

### 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 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
7. def clone(): AnyRef
Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
8. val denominator: Double
9. final def eq(arg0: AnyRef): Boolean
Definition Classes
AnyRef
10. def equals(arg0: Any): Boolean
Definition Classes
AnyRef → Any
11. val evidence: List[NamedEvidence[_]]
12. def finalize(): Unit
Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
13. final def getClass(): Class[_]
Definition Classes
AnyRef → Any
14. def hashCode(): Int
Definition Classes
AnyRef → Any
15. 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
16. def isActive: Boolean
Definition Classes
Algorithm
17. final def isInstanceOf[T0]: Boolean
Definition Classes
Any
18. 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
19. def logProbEvidence: Double

The computed log probability of evidence.

20. final def ne(arg0: AnyRef): Boolean
Definition Classes
AnyRef
21. final def notify(): Unit
Definition Classes
AnyRef
22. final def notifyAll(): Unit
Definition Classes
AnyRef

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

24. def probEvidence: Double

The computed probability of evidence.

25. 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
26. 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
27. 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
28. final def synchronized[T0](arg0: ⇒ T0): T0
Definition Classes
AnyRef
29. def toString(): String
Definition Classes
AnyRef → Any
30. final def wait(): Unit
Definition Classes
AnyRef
Annotations
@throws( ... )
31. final def wait(arg0: Long, arg1: Int): Unit
Definition Classes
AnyRef
Annotations
@throws( ... )
32. final def wait(arg0: Long): Unit
Definition Classes
AnyRef
Annotations
@throws( ... )