dk.bayes.dsl.variable.gaussian.multivariatelinear

MultivariateLinearGaussian

Related Docs: object MultivariateLinearGaussian | package multivariatelinear

class MultivariateLinearGaussian extends Gaussian with MultivariateLinearGaussianFactor

y = A*x + b + gaussian_noise

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Inherited
  1. MultivariateLinearGaussian
  2. MultivariateLinearGaussianFactor
  3. DoubleFactor
  4. Gaussian
  5. Variable
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Instance Constructors

  1. new MultivariateLinearGaussian(a: DenseMatrix[Double], x: MultivariateGaussian, b: DenseVector[Double], v: DenseMatrix[Double], yValue: Option[DenseVector[Double]])

Value Members

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

    Definition Classes
    AnyRef → Any
  2. final def ##(): Int

    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any
  4. val a: DenseMatrix[Double]

  5. def addChild(v: Variable): ListBuffer[Variable]

    Definition Classes
    Variable
  6. final def asInstanceOf[T0]: T0

    Definition Classes
    Any
  7. val b: DenseVector[Double]

  8. def calcYFactorMsgUp(x: DenseCanonicalGaussian, oldFactorMsgUp: DenseCanonicalGaussian): Option[DenseCanonicalGaussian]

    x

    Marginal of variable x

    oldFactorMsgUp

    old message sent from factor y=f(x) to variable X

    Definition Classes
    MultivariateLinearGaussianFactorDoubleFactor
  9. var children: ListBuffer[Variable]

    Implementation

    Implementation

    Definition Classes
    Variable
  10. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  11. final def eq(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  12. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any
  13. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  14. def getAllVariables(): Seq[Variable]

    Returns all variables in the Bayesian Network

    Returns all variables in the Bayesian Network

    Definition Classes
    Variable
  15. def getChildren(): Seq[Variable]

    Definition Classes
    Variable
  16. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any
  17. def getParents(): Seq[Variable]

    Definitions

    Definitions

    Definition Classes
    MultivariateLinearGaussianVariable
  18. def hasChildren(): Boolean

    Definition Classes
    Variable
  19. def hasParents(): Boolean

    Definition Classes
    Variable
  20. def hashCode(): Int

    Definition Classes
    AnyRef → Any
  21. val initFactorMsgUp: DenseCanonicalGaussian

  22. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any
  23. final def ne(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  24. final def notify(): Unit

    Definition Classes
    AnyRef
  25. final def notifyAll(): Unit

    Definition Classes
    AnyRef
  26. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes
    AnyRef
  27. def toString(): String

    Definition Classes
    AnyRef → Any
  28. val v: DenseMatrix[Double]

  29. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  30. final def wait(arg0: Long, arg1: Int): Unit

    Definition Classes
    AnyRef
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    @throws( ... )
  31. final def wait(arg0: Long): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  32. val x: MultivariateGaussian

  33. val yValue: Option[DenseVector[Double]]

Inherited from DoubleFactor[DenseCanonicalGaussian, Any]

Inherited from Gaussian

Inherited from Variable

Inherited from AnyRef

Inherited from Any

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