case class NormalVec(mu: VectoD, cov: MatrixD, stream: Int = 0) extends VariateVec with Product with Serializable
The NormalVec 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://onlinelibrary.wiley.com/doi/10.1111/1467-9639.00037/pdf
http://www.statlect.com/mcdnrm1.htm
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Instance Constructors
- new NormalVec(mu: VectoD, cov: MatrixD, stream: Int = 0)
- mu
the mean vector
- cov
the covariance matrix
- stream
the random number stream
Value Members
- final def !=(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
- final def ##: Int
- Definition Classes
- AnyRef → Any
- final def ==(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
- 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
- final def asInstanceOf[T0]: T0
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- def clone(): AnyRef
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- protected[lang]
- Definition Classes
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- @throws(classOf[java.lang.CloneNotSupportedException]) @native() @HotSpotIntrinsicCandidate()
- val cov: MatrixD
- def discrete: Boolean
Determine whether the distribution is discrete or continuous.
Determine whether the distribution is discrete or continuous.
- Definition Classes
- VariateVec
- final def eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
- final def flaw(method: String, message: String): Unit
- Definition Classes
- Error
- def gen: VectoD
Determine the next random double vector for the particular distribution.
Determine the next random double vector for the particular distribution.
- Definition Classes
- NormalVec → VariateVec
- final def getClass(): Class[_ <: AnyRef]
- Definition Classes
- AnyRef → Any
- Annotations
- @native() @HotSpotIntrinsicCandidate()
- def igen: VectoI
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
- NormalVec → VariateVec
- final def isInstanceOf[T0]: Boolean
- Definition Classes
- Any
- def mean: VectoD
Compute the vector mean for the particular distribution.
Compute the vector mean for the particular distribution.
- Definition Classes
- NormalVec → VariateVec
- val mu: VectoD
- final def ne(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
- final def notify(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native() @HotSpotIntrinsicCandidate()
- final def notifyAll(): Unit
- Definition Classes
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- Annotations
- @native() @HotSpotIntrinsicCandidate()
- def pf(z: VectoD): 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
- NormalVec → VariateVec
- def productElementNames: Iterator[String]
- Definition Classes
- Product
- val r: Random
Random number stream selected by the stream number
Random number stream selected by the stream number
- Attributes
- protected
- Definition Classes
- VariateVec
- val stream: Int
- final def synchronized[T0](arg0: => T0): T0
- Definition Classes
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- final def wait(arg0: Long, arg1: Int): Unit
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- @throws(classOf[java.lang.InterruptedException])
- final def wait(arg0: Long): Unit
- Definition Classes
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- Annotations
- @throws(classOf[java.lang.InterruptedException]) @native()
- final def wait(): Unit
- Definition Classes
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- @throws(classOf[java.lang.InterruptedException])
Deprecated Value Members
- def finalize(): Unit
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- protected[lang]
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- @throws(classOf[java.lang.Throwable]) @Deprecated
- Deprecated