BayesianNetwork

Value Members

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

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

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3. final def ##(): Int

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

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

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

7. final def asInstanceOf[T0]: T0

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8. def clone(): AnyRef

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9. def computeFullCase(c: List[CaseIs[T]]): Double

10. def cpt(variable: RandomVariable[T]): stats.FactorModule.Factor[T]

11. def duplicate: BayesianNetwork[T]

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

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

14. def factorElimination1(Q: Set[RandomVariable[T]]): stats.FactorModule.Factor[T]

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

16. def finalize(): Unit

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

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18. val graph: DirectedGraph[BayesianNetworkNode[T], String]

19. def interactionGraph: InteractionGraph[T]

    interactionGraph

    interactionGraph

    Also called the "moral graph"

20. def interactsWith(v1: RandomVariable[T], v2: RandomVariable[T]): Boolean

21. final def isInstanceOf[T0]: Boolean

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22. def jointProbabilityTable: stats.FactorModule.Factor[T]

23. def markovAssumptionsFor(rv: RandomVariable[T]): Independence[T]

24. val name: String

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

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26. final def notify(): Unit

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27. final def notifyAll(): Unit

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28. def numVariables: Int

29. def orderWidth(order: List[RandomVariable[T]]): Int

    orderWidth

    orderWidth

    Chapter 6 Algorithm 2 (page 13)

30. def probabilityOf(cs: Seq[CaseIs[T]]): Real

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

    pruneEdges

    pruneEdges

    6.8.2

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

    pruneNetworkVarsAndEdges

    pruneNetworkVarsAndEdges

    6.8.3

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

34. def randomVariables: Vector[RandomVariable[T]]

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

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36. def variableEliminationMAP(Q: Set[RandomVariable[T]], e: List[RandomVariable[T]]): List[CaseIs[T]]

    variableEliminationMAP

    variableEliminationMAP

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

    see ch 6 page 31: Algorithm 8

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

    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

38. def variableEliminationPriorMarginalII(Q: Set[RandomVariable[T]], π: List[RandomVariable[T]], e: CaseIs[T]): stats.FactorModule.Factor[T]

    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

39. final def wait(): Unit

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

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

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