BPFactor

abstract def calculateBeliefsTensor: Tensor

Unnormalized log scores over values of varying neighbors

abstract def edges: Seq[BPEdge]

abstract def factor: model.Factor

abstract def scores: Tensor

abstract def summary: BPSummary

abstract def tensorStatistics: Tensor

abstract def updateOutgoing(e: BPEdge): Unit

Re-calculate the message from this factor to edge "e" and set e.

final def !=(arg0: AnyRef): Boolean

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: AnyRef): Boolean

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def betheObjective: Double

Normalized probabilities over values of only the varying neighbors, in the form of a Proportions

def calculateLogZ: Double

The logSum of all entries in the beliefs tensor

def calculateMarginalTensor: Tensor

Normalized probabilities over values of varying neighbors

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

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

def toString(): String

def updateOutgoing(): Unit

final def wait(): Unit

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

final def wait(arg0: Long): Unit