BayesianNetwork

Value Members

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

   

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2. final def ##(): Int

   

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

   

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4. def _factorElimination1(Q: Set[Distribution[T, N]], S: List[Factor[T, N]]): Factor[T, N]

   

5. val _graph: DG

   

6. final def asInstanceOf[T0]: T0

   

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7. val bnnByVariable: Map[Distribution[T, N], BayesianNetworkNode[T, N]]

   

8. val bnns: List[BayesianNetworkNode[T, N]]

   

9. def clone(): AnyRef

   

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10. def computeFullCase(c: List[CaseIs[T, N]]): Double

    

11. def cpt(variable: Distribution[T, N]): Factor[T, N]

    

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

    

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13. def factorElimination[UG](τ: EliminationTree[T, N, UG], e: List[CaseIs[T, N]])(implicit ug: UndirectedGraph[UG, Factor[T, N], EliminationTreeEdge]): Map[Factor[T, N], Factor[T, N]]

    

14. def factorElimination1(Q: Set[Distribution[T, N]]): Factor[T, N]

    

15. def factorElimination2[UG](Q: Set[Distribution[T, N]], τ: EliminationTree[T, N, UG], f: Factor[T, N])(implicit ug: UndirectedGraph[UG, Factor[T, N], EliminationTreeEdge]): (BayesianNetwork[T, N, DG], Factor[T, N])

    

16. def finalize(): Unit

    

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

    

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18. def graph: DG

    

19. def interactionGraph[UG](implicit ug: UndirectedGraph[UG, Distribution[T, N], InteractionGraphEdge]): InteractionGraph[T, N, UG]

    

    interactionGraph

    interactionGraph

    Also called the "moral graph"

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

    

21. final def isInstanceOf[T0]: Boolean

    

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22. def jointProbabilityTable: Factor[T, N]

    

23. def markovAssumptionsFor(rv: Distribution[T, N]): Independence[T, N]

    

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[UG](order: List[Distribution[T, N]])(implicit ug: UndirectedGraph[UG, Distribution[T, N], InteractionGraphEdge]): Int

    

    orderWidth

    orderWidth

    Chapter 6 Algorithm 2 (page 13)

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

    

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

    

    pruneEdges

    pruneEdges

    6.8.2

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

    

    pruneNetworkVarsAndEdges

    pruneNetworkVarsAndEdges

    6.8.3

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

    

34. def randomVariables: Vector[Distribution[T, N]]

    

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

    

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

    

    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[Distribution[T, N]], π: List[Distribution[T, N]]): Factor[T, N]

    

    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[Distribution[T, N]], π: List[Distribution[T, N]], e: CaseIs[T, N]): Factor[T, N]

    

    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. val variableFactorMap: Map[Distribution[T, N], Factor[T, N]]

    

40. final def wait(): Unit

    

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

    

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

    

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