dk.bayes.math.gaussian

MultivariateGaussian

Related Docs: object MultivariateGaussian | package gaussian

case class MultivariateGaussian(m: DenseVector[Double], v: DenseMatrix[Double]) extends Product with Serializable

Multivariate Gaussian from the book 'Christopher M. Bishop. Pattern Recognition and Machine Learning (Information Science and Statistics), 2009'

Linear Supertypes
Serializable, Serializable, Product, Equals, AnyRef, Any
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  1. MultivariateGaussian
  2. Serializable
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  4. Product
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Instance Constructors

  1. new MultivariateGaussian(m: DenseVector[Double], v: DenseMatrix[Double])

Value Members

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

    Definition Classes
    AnyRef → Any

  2. final def ##(): Int

    Definition Classes
    AnyRef → Any

  3. def *(linearGaussian: LinearGaussian): MultivariateGaussian

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

    Definition Classes
    AnyRef → Any

  5. final def asInstanceOf[T0]: T0

    Definition Classes
    Any

  6. def clone(): AnyRef

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

  7. def draw(randSeed: Int): Array[Double]

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

    Definition Classes
    AnyRef

  9. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )

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

    Definition Classes
    AnyRef → Any

  11. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

  12. val m: DenseVector[Double]

  13. def marginal(varIndex: Int): Gaussian

    Returns Gaussian marginal for a random variable at a given position

  14. def marginalise(varIndex: Int): MultivariateGaussian

    Returns Gaussian, marginalising out given variable

    Returns Gaussian, marginalising out given variable

    varIndex

    Index of a variable, which is marginalised out

  15. final def ne(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

  16. def normConstant(): Double

  17. final def notify(): Unit

    Definition Classes
    AnyRef

  18. final def notifyAll(): Unit

    Definition Classes
    AnyRef

  19. def pdf(x: DenseVector[Double]): Double

    Returns the value of probability density function for a given value of vector x.

  20. def pdf(x: Double): Double

    Returns the value of probability density function for a given value of x.

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

    Definition Classes
    AnyRef

  22. def toGaussian(): Gaussian

  23. val v: DenseMatrix[Double]

  24. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  27. def withEvidence(varIndex: Int, value: Double): MultivariateGaussian

Inherited from Serializable

Inherited from Serializable

Inherited from Product

Inherited from Equals

Inherited from AnyRef

Inherited from Any

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