BayesianNetwork

Value Members

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

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

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3. def ## (): Int

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

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

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6. def _findOpenPath (visited: Map[axle.stats.RandomVariable[_], Set[axle.stats.RandomVariable[_]]], priorDirection: Int, prior: axle.stats.RandomVariable[_], current: Set[axle.stats.RandomVariable[_]], to: Set[axle.stats.RandomVariable[_]], given: Set[axle.stats.RandomVariable[_]]): Option[List[axle.stats.RandomVariable[_]]]

   Definition Classes

   Model

7. def asInstanceOf [T0] : T0

   Attributes

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

   Definition Classes

   Model

9. def clone (): AnyRef

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   @throws()

10. def computeFullCase (c: List[axle.stats.CaseIs[_]]): Double

11. def cpt (variable: axle.stats.RandomVariable[_]): Factor

12. def eq (arg0: AnyRef): Boolean

    Attributes

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13. def equals (arg0: Any): Boolean

    Definition Classes

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14. def factorElimination (τ: EliminationTree, e: List[axle.stats.CaseIs[_]]): Map[Factor, Factor]

15. def factorElimination1 (Q: Set[axle.stats.RandomVariable[_]]): Factor

16. def factorElimination2 (Q: Set[axle.stats.RandomVariable[_]], τ: EliminationTree, f: Factor): (BayesianNetwork, Factor)

17. def finalize (): Unit

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18. def getClass (): java.lang.Class[_]

    Attributes

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

20. def hashCode (): Int

    Definition Classes

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21. def interactionGraph (): InteractionGraph

    interactionGraph

    interactionGraph

    Also called the "moral graph"

22. def interactsWith (v1: axle.stats.RandomVariable[_], v2: axle.stats.RandomVariable[_]): Boolean

23. def isInstanceOf [T0] : Boolean

    Attributes

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24. def jointProbabilityTable (): Factor

25. def markovAssumptionsFor (rv: axle.stats.RandomVariable[_]): Independence

26. def name (): String

    Definition Classes

    BayesianNetwork → Model

27. def ne (arg0: AnyRef): Boolean

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28. def notify (): Unit

    Attributes

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29. def notifyAll (): Unit

    Attributes

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    Definition Classes

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30. def numVariables (): Int

    Definition Classes

    Model

31. def orderWidth (order: List[axle.stats.RandomVariable[_]]): Int

    orderWidth

    orderWidth

    Chapter 6 Algorithm 2 (page 13)

32. def probabilityOf (cs: Seq[axle.stats.CaseIs[_]]): Double

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

    pruneEdges

    pruneEdges

    6.8.2

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

    pruneNetworkVarsAndEdges

    pruneNetworkVarsAndEdges

    6.8.3

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

36. def randomVariables (): List[axle.stats.RandomVariable[_]]

    Definition Classes

    Model

37. def synchronized [T0] (arg0: ⇒ T0): T0

    Attributes

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38. def toString (): String

    Definition Classes

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39. def variable (name: String): axle.stats.RandomVariable[_]

    Definition Classes

    Model

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

    variableEliminationMAP

    variableEliminationMAP

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

    see ch 6 page 31: Algorithm 8

41. def variableEliminationPriorMarginalI (Q: Set[axle.stats.RandomVariable[_]], π: List[axle.stats.RandomVariable[_]]): 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

42. def variableEliminationPriorMarginalII [A] (Q: Set[axle.stats.RandomVariable[_]], π: List[axle.stats.RandomVariable[_]], e: CaseIs[A]): 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

43. def vertexPayloadToRandomVariable (mvp: BayesianNetworkNode): axle.stats.RandomVariable[_]

    Definition Classes

    BayesianNetwork → Model

44. def wait (): Unit

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

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46. def wait (arg0: Long): Unit

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    @throws()