axle.stats

BayesianNetwork

class BayesianNetwork extends Model[BayesianNetworkNode]

Linear Supertypes
Model[BayesianNetworkNode], JungDirectedGraph[BayesianNetworkNode, String], DirectedGraph[BayesianNetworkNode, String], Graph[BayesianNetworkNode, String], AnyRef, Any
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  1. BayesianNetwork
  2. Model
  3. JungDirectedGraph
  4. DirectedGraph
  5. Graph
  6. AnyRef
  7. Any
Visibility
  1. Public
  2. All

Instance Constructors

  1. new BayesianNetwork (_name: String)

Type Members

  1. trait DirectedGraphEdge [P] extends GraphEdge[P]

  2. trait DirectedGraphVertex [P] extends GraphVertex[P]

  3. type E = JungDirectedGraphEdge[String]

    Definition Classes
    JungDirectedGraph → DirectedGraph → Graph

  4. trait GraphEdge [P] extends AnyRef

  5. trait GraphVertex [P] extends AnyRef

  6. trait JungDirectedGraphEdge [P] extends DirectedGraphEdge[P]

  7. class JungDirectedGraphEdgeImpl extends JungDirectedGraphEdge[EP]

  8. trait JungDirectedGraphVertex [P] extends DirectedGraphVertex[P]

  9. class JungDirectedGraphVertexImpl extends JungDirectedGraphVertex[VP]

  10. type S = DirectedSparseGraph[V, E]

    Definition Classes
    JungDirectedGraph → Graph

  11. type V = JungDirectedGraphVertex[BayesianNetworkNode]

    Definition Classes
    JungDirectedGraph → DirectedGraph → Graph

Value Members

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

    Attributes
    final
    Definition Classes
    AnyRef

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

    Attributes
    final
    Definition Classes
    Any

  3. def ## (): Int

    Attributes
    final
    Definition Classes
    AnyRef → Any

  4. def ++= (vps: Seq[BayesianNetworkNode]): Seq[V]

    Definition Classes
    Graph

  5. def += (vp: BayesianNetworkNode): V

    Definition Classes
    Graph

  6. def += (vs: (V, V), ep: String): E

    Definition Classes
    Graph

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

    Attributes
    final
    Definition Classes
    AnyRef

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

    Attributes
    final
    Definition Classes
    Any

  9. def _ancestors (v: V, result: Set[V]): Unit

    Definition Classes
    DirectedGraph

  10. def _descendants (v: V, result: Set[V]): Unit

    Definition Classes
    DirectedGraph

  11. 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

  12. def ancestors (vs: Set[V]): Set[V]

    Definition Classes
    DirectedGraph

  13. def ancestors (v: V): Set[V]

    Definition Classes
    DirectedGraph

  14. def asInstanceOf [T0] : T0

    Attributes
    final
    Definition Classes
    Any

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

    Definition Classes
    Model

  16. def clone (): AnyRef

    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws()

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

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

  19. def deleteEdge (e: E): Unit

    Definition Classes
    JungDirectedGraph → DirectedGraph

  20. def deleteVertex (v: V): Unit

    Definition Classes
    JungDirectedGraph → DirectedGraph

  21. def descendants (v: V): Set[V]

    Definition Classes
    DirectedGraph

  22. def descendantsIntersectsSet (v: V, s: Set[V]): Boolean

    Definition Classes
    JungDirectedGraph → DirectedGraph

  23. def duplicate (): BayesianNetwork

  24. def edge (source: V, dest: V, payload: String): E

    Definition Classes
    JungDirectedGraph → Graph

  25. def edges (): Set[E]

    Definition Classes
    JungDirectedGraph → Graph

  26. def eq (arg0: AnyRef): Boolean

    Attributes
    final
    Definition Classes
    AnyRef

  27. def equals (arg0: Any): Boolean

    Definition Classes
    AnyRef → Any

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

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

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

  31. def factorElimination3 (Q: Set[axle.stats.RandomVariable[_]], τ: EliminationTree, f: Factor): Factor

  32. def finalize (): Unit

    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws()

  33. def findEdge (from: V, to: V): Option[E]

    Definition Classes
    JungDirectedGraph → DirectedGraph

  34. def findVertex (test: (BayesianNetworkNode) ⇒ Boolean): Option[V]

    Definition Classes
    JungDirectedGraph

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

    Attributes
    final
    Definition Classes
    AnyRef → Any

  36. def hashCode (): Int

    Definition Classes
    AnyRef → Any

  37. def interactionGraph (): InteractionGraph

    interactionGraph

    interactionGraph

    Also called the "moral graph"

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

  39. def isAcyclic (): Boolean

    Definition Classes
    JungDirectedGraph → DirectedGraph

  40. def isInstanceOf [T0] : Boolean

    Attributes
    final
    Definition Classes
    Any

  41. def isLeaf (v: V): Boolean

    Definition Classes
    JungDirectedGraph → DirectedGraph

  42. def jointProbabilityTable (): Factor

  43. val jungGraph : DirectedSparseGraph[V, E]

    Definition Classes
    JungDirectedGraph

  44. def leaves (): Set[V]

    Definition Classes
    JungDirectedGraph → DirectedGraph

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

  46. def minDegreeOrder (pX: Set[axle.stats.RandomVariable[_]]): List[axle.stats.RandomVariable[_]]

  47. def minFillOrder (pX: Set[axle.stats.RandomVariable[_]]): List[axle.stats.RandomVariable[_]]

  48. def moralGraph (): axle.graph.JungUndirectedGraphFactory.UndirectedGraph[_, _]

    Definition Classes
    JungDirectedGraph

  49. def name (): String

    Definition Classes
    BayesianNetwork → Model

  50. val name2variable : Map[String, axle.stats.RandomVariable[_]]

    Definition Classes
    Model

  51. def ne (arg0: AnyRef): Boolean

    Attributes
    final
    Definition Classes
    AnyRef

  52. def neighbors (v: V): Set[V]

    Definition Classes
    JungDirectedGraph → DirectedGraph

  53. var newVarIndex : Int

    Definition Classes
    Model

  54. def notify (): Unit

    Attributes
    final
    Definition Classes
    AnyRef

  55. def notifyAll (): Unit

    Attributes
    final
    Definition Classes
    AnyRef

  56. def numVariables (): Int

    Definition Classes
    Model

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

    orderWidth

    orderWidth

    Chapter 6 Algorithm 2 (page 13)

  58. def outputEdgesOf (v: V): Set[E]

    Definition Classes
    JungDirectedGraph → DirectedGraph

  59. def precedes (v1: V, v2: V): Boolean

    Definition Classes
    JungDirectedGraph → DirectedGraph

  60. def predecessors (v: V): Set[V]

    Definition Classes
    JungDirectedGraph → DirectedGraph

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

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

    pruneEdges

    pruneEdges

    6.8.2

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

    pruneNetworkVarsAndEdges

    pruneNetworkVarsAndEdges

    6.8.3

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

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

    Definition Classes
    Model

  66. def removeAllEdgesAndVertices (): Unit

    Definition Classes
    JungDirectedGraph

  67. def removeInputs (vs: Set[V]): Unit

    Definition Classes
    JungDirectedGraph → DirectedGraph

  68. def removeOutputs (vs: Set[V]): Unit

    Definition Classes
    JungDirectedGraph → DirectedGraph

  69. def removePredecessor (v: V, predecessor: V): Unit

    Definition Classes
    JungDirectedGraph → DirectedGraph

  70. def removeSuccessor (v: V, successor: V): Unit

    Definition Classes
    JungDirectedGraph → DirectedGraph

  71. def shortestPath (source: V, goal: V): Option[List[E]]

    Definition Classes
    JungDirectedGraph → DirectedGraph

  72. def size (): Int

    Definition Classes
    JungDirectedGraph → Graph

  73. def storage (): DirectedSparseGraph[V, E]

    Definition Classes
    JungDirectedGraph → Graph

  74. def successors (v: V): Set[V]

    Definition Classes
    JungDirectedGraph → DirectedGraph

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

    Attributes
    final
    Definition Classes
    AnyRef

  76. def toString (): String

    Definition Classes
    AnyRef → Any

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

    Definition Classes
    Model

  78. 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

  79. def variableEliminationMPE (e: List[axle.stats.CaseIs[_]]): (Double, BayesianNetwork)

  80. def variableEliminationPR (Q: Set[axle.stats.RandomVariable[_]], eOpt: Option[List[axle.stats.CaseIs[_]]]): (Factor, BayesianNetwork)

  81. 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

  82. 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

  83. def vertex (payload: BayesianNetworkNode): V

    Definition Classes
    JungDirectedGraph → Graph

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

    Definition Classes
    BayesianNetwork → Model

  85. def vertexToVisualizationHtml (vp: BayesianNetworkNode): Node

    Definition Classes
    BayesianNetwork → JungDirectedGraph

  86. def vertices (): Set[V]

    Definition Classes
    JungDirectedGraph → Graph

  87. def wait (): Unit

    Attributes
    final
    Definition Classes
    AnyRef
    Annotations
    @throws()

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

    Attributes
    final
    Definition Classes
    AnyRef
    Annotations
    @throws()

  89. def wait (arg0: Long): Unit

    Attributes
    final
    Definition Classes
    AnyRef
    Annotations
    @throws()

Inherited from Model[BayesianNetworkNode]

Inherited from JungDirectedGraph[BayesianNetworkNode, String]

Inherited from DirectedGraph[BayesianNetworkNode, String]

Inherited from Graph[BayesianNetworkNode, String]

Inherited from AnyRef

Inherited from Any