BPFactor1

abstract def factor: model.Factor

abstract def hasLimitedDiscreteValues1: Boolean

abstract def limitedDiscreteValues1: SparseBinaryTensor1

abstract def scores: la.Tensor1

abstract def tensorStatistics: Tensor

final def !=(arg0: AnyRef): Boolean

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: AnyRef): Boolean

final def ==(arg0: Any): Boolean

val _1: DiscreteVar

final def asInstanceOf[T0]: T0

def betheObjective: Double

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

def calculateBeliefsTensor: la.Tensor1

Unnormalized log scores over values of varying neighbors

def calculateLogZ: Double

The logSum of all entries in the beliefs tensor

def calculateMarginalTensor: Tensor

Normalized probabilities over values of varying neighbors

def calculateOutgoing1: la.Tensor1

def clone(): AnyRef

val edge1: BPEdge

val edges: Seq[BPEdge]

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

def incrementCurrentValue(w: Double): Unit

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def proportions: Proportions1

def setToMaximize(implicit d: variable.DiffList): Unit

val summary: BPSummary

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

def toString(): String

def updateOutgoing(): Unit

def updateOutgoing(e: BPEdge): Unit

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

def updateOutgoing1(): Unit

def value1: Value

def variables: Seq[DiscreteVar]

final def wait(): Unit

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

final def wait(arg0: Long): Unit