BayesianNetwork

Value Members

1. final def !=(arg0: AnyRef): Boolean

   Definition Classes

   AnyRef

2. final def !=(arg0: Any): Boolean

   Definition Classes

   Any

3. final def ##(): Int

   Definition Classes

   AnyRef → Any

4. final def ==(arg0: AnyRef): Boolean

   Definition Classes

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5. final def ==(arg0: Any): Boolean

   Definition Classes

   Any

6. def _factorElimination1(Q: Set[RandomVariable[_]], S: List[stats.FactorModule.Factor]): stats.FactorModule.Factor

7. def _findOpenPath(visited: Map[RandomVariable[_], Set[RandomVariable[_]]], priorDirection: Int, priorOpt: Option[RandomVariable[_]], current: Set[RandomVariable[_]], to: Set[RandomVariable[_]], given: Set[RandomVariable[_]]): Option[List[RandomVariable[_]]]

   Definition Classes

   Model

8. final def asInstanceOf[T0]: T0

   Definition Classes

   Any

9. def blocks(from: Set[RandomVariable[_]], to: Set[RandomVariable[_]], given: Set[RandomVariable[_]]): Boolean

   Definition Classes

   Model

10. def clone(): AnyRef

    Attributes

    protected[java.lang]

    Definition Classes

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    @throws()

11. def computeFullCase(c: List[CaseIs[_]]): Double

12. def cpt(variable: RandomVariable[_]): stats.FactorModule.Factor

13. final def eq(arg0: AnyRef): Boolean

    Definition Classes

    AnyRef

14. def equals(arg0: Any): Boolean

    Definition Classes

    AnyRef → Any

15. def factorElimination(τ: EliminationTreeModule.EliminationTree, e: List[CaseIs[_]]): Map[stats.FactorModule.Factor, stats.FactorModule.Factor]

16. def factorElimination1(Q: Set[RandomVariable[_]]): stats.FactorModule.Factor

17. def factorElimination2(Q: Set[RandomVariable[_]], τ: EliminationTreeModule.EliminationTree, f: stats.FactorModule.Factor): (BayesianNetwork, stats.FactorModule.Factor)

18. def finalize(): Unit

    Attributes

    protected[java.lang]

    Definition Classes

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    @throws()

19. final def getClass(): Class[_]

    Definition Classes

    AnyRef → Any

20. def graph(): DirectedGraph[BayesianNetworkNode, String]

21. def hashCode(): Int

    Definition Classes

    AnyRef → Any

22. def interactionGraph(): InteractionGraph

    interactionGraph

    interactionGraph

    Also called the "moral graph"

23. def interactsWith(v1: RandomVariable[_], v2: RandomVariable[_]): Boolean

24. final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

25. def jointProbabilityTable(): stats.FactorModule.Factor

26. def markovAssumptionsFor(rv: RandomVariable[_]): Independence

27. def name(): String

    Definition Classes

    BayesianNetwork → Model

28. final def ne(arg0: AnyRef): Boolean

    Definition Classes

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29. final def notify(): Unit

    Definition Classes

    AnyRef

30. final def notifyAll(): Unit

    Definition Classes

    AnyRef

31. def numVariables(): Int

    Definition Classes

    Model

32. def orderWidth(order: List[RandomVariable[_]]): Int

    orderWidth

    orderWidth

    Chapter 6 Algorithm 2 (page 13)

33. def probabilityOf(cs: Seq[CaseIs[_]]): Double

34. def pruneEdges(resultName: String, eOpt: Option[List[CaseIs[_]]]): BayesianNetwork

    pruneEdges

    pruneEdges

    6.8.2

35. def pruneNetworkVarsAndEdges(Q: Set[RandomVariable[_]], eOpt: Option[List[CaseIs[_]]]): BayesianNetwork

    pruneNetworkVarsAndEdges

    pruneNetworkVarsAndEdges

    6.8.3

36. def pruneNodes(Q: Set[RandomVariable[_]], eOpt: Option[List[CaseIs[_]]], g: BayesianNetwork): BayesianNetwork

37. def randomVariables(): List[RandomVariable[_]]

    Definition Classes

    Model

38. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes

    AnyRef

39. def toString(): String

    Definition Classes

    AnyRef → Any

40. def variable(name: String): RandomVariable[_]

    Definition Classes

    Model

41. def variableEliminationMAP(Q: Set[RandomVariable[_]], e: List[RandomVariable[_]]): List[CaseIs[_]]

    variableEliminationMAP

    variableEliminationMAP

    returns an instantiation q which maximizes Pr(q,e) and that probability

    see ch 6 page 31: Algorithm 8

42. def variableEliminationPriorMarginalI(Q: Set[RandomVariable[_]], π: List[RandomVariable[_]]): stats.FactorModule.Factor

    Algorithm 1 from Chapter 6 (page 9)

    Algorithm 1 from Chapter 6 (page 9)

    Q

    is a set of variables

    π

    is an ordered list of the variables not in Q

    returns

    the prior marginal pr(Q)

    The cost is the cost of the Tk multiplication. This is highly dependent on π

43. def variableEliminationPriorMarginalII[A](Q: Set[RandomVariable[_]], π: List[RandomVariable[_]], e: CaseIs[A]): stats.FactorModule.Factor

    Chapter 6 Algorithm 5 (page 17)

    Chapter 6 Algorithm 5 (page 17)

    assert: Q subset of variables assert: π ordering of variables in S but not in Q assert: e assigns values to variables in this network

44. def vertexPayloadToRandomVariable(mvp: BayesianNetworkNode): RandomVariable[_]

    Definition Classes

    BayesianNetwork → Model

45. final def wait(): Unit

    Definition Classes

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    @throws()

46. final def wait(arg0: Long, arg1: Int): Unit

    Definition Classes

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    @throws()

47. final def wait(arg0: Long): Unit

    Definition Classes

    AnyRef

    Annotations

    @throws()