scalation.random

NormalVec

case class NormalVec(mu: VectorD, cov: MatrixD, stream: Int) extends VariateVec with Product with Serializable

This class generates Normal (Gaussian) random variate vectors according to the Multivariate Normal distribution with mean 'mu' and covariance 'cov'. This continuous RVV models normally distributed multidimensional data.

mu

the mean vector

cov

the covariance matrix

stream

the random number stream

See also

http://www.statlect.com/mcdnrm1.htm

,

http://onlinelibrary.wiley.com/doi/10.1111/1467-9639.00037/pdf

Linear Supertypes
Serializable, Serializable, Product, Equals, VariateVec, Error, AnyRef, Any
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  1. NormalVec
  2. Serializable
  3. Serializable
  4. Product
  5. Equals
  6. VariateVec
  7. Error
  8. AnyRef
  9. Any
Visibility
  1. Public
  2. All

Instance Constructors

  1. new NormalVec(mu: VectorD, cov: MatrixD, stream: Int = 0)

    mu

    the mean vector

    cov

    the covariance matrix

    stream

    the random number stream

Value Members

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

    Definition Classes
    AnyRef
  2. final def !=(arg0: Any): Boolean

    Definition Classes
    Any
  3. final def ##(): Int

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

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

    Definition Classes
    Any
  6. var _discrete: Boolean

    Indicates whether the distribution is discrete or continuous (default)

    Indicates whether the distribution is discrete or continuous (default)

    Attributes
    protected
    Definition Classes
    VariateVec
  7. final def asInstanceOf[T0]: T0

    Definition Classes
    Any
  8. def canEqual(arg0: Any): Boolean

    Definition Classes
    NormalVec → Equals
  9. def clone(): AnyRef

    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws()
  10. val cov: MatrixD

    the covariance matrix

  11. def discrete: Boolean

    Determine whether the distribution is discrete or continuous.

    Determine whether the distribution is discrete or continuous.

    Definition Classes
    VariateVec
  12. final def eq(arg0: AnyRef): Boolean

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

    Definition Classes
    NormalVec → Equals → AnyRef → Any
  14. def finalize(): Unit

    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws()
  15. def flaw(method: String, message: String): Unit

    Show the flaw by printing the error message.

    Show the flaw by printing the error message.

    method

    the method where the error occurred

    message

    the error message

    Definition Classes
    Error
  16. def gen: VectorD

    Determine the next random double vector for the particular distribution.

    Determine the next random double vector for the particular distribution.

    Definition Classes
    NormalVecVariateVec
  17. final def getClass(): java.lang.Class[_]

    Definition Classes
    AnyRef → Any
  18. def hashCode(): Int

    Definition Classes
    NormalVec → AnyRef → Any
  19. def igen: VectorI

    Determine the next random integer vector for the particular distribution.

    Determine the next random integer vector for the particular distribution. It is only valid for discrete random variates.

    Definition Classes
    NormalVecVariateVec
  20. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any
  21. def mean: VectorD

    Compute the vector mean for the particular distribution.

    Compute the vector mean for the particular distribution.

    Definition Classes
    NormalVecVariateVec
  22. val mu: VectorD

    the mean vector

  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. def pf(z: VectorD): Double

    Compute the probability function (pf): The probability density function (pdf) for continuous RVV's or the probability mass function (pmf) for discrete RVV's.

    Compute the probability function (pf): The probability density function (pdf) for continuous RVV's or the probability mass function (pmf) for discrete RVV's.

    z

    the mass point/vector whose probability is sought

    Definition Classes
    NormalVecVariateVec
  27. def productArity: Int

    Definition Classes
    NormalVec → Product
  28. def productElement(arg0: Int): Any

    Definition Classes
    NormalVec → Product
  29. def productIterator: Iterator[Any]

    Definition Classes
    Product
  30. def productPrefix: String

    Definition Classes
    NormalVec → Product
  31. val r: Random

    Random number stream selected by the stream number

    Random number stream selected by the stream number

    Attributes
    protected
    Definition Classes
    VariateVec
  32. val stream: Int

    the random number stream

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

    Definition Classes
    AnyRef
  34. def toString(): String

    Definition Classes
    NormalVec → AnyRef → Any
  35. final def wait(): Unit

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

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

    Definition Classes
    AnyRef
    Annotations
    @throws()

Deprecated Value Members

  1. def productElements: Iterator[Any]

    Definition Classes
    Product
    Annotations
    @deprecated
    Deprecated

    (Since version 2.8.0) use productIterator instead

Inherited from Serializable

Inherited from Serializable

Inherited from Product

Inherited from Equals

Inherited from VariateVec

Inherited from Error

Inherited from AnyRef

Inherited from Any