MultStudentsTPRV

Instance Constructors

new MultStudentsTPRV(mu: Double, mean: PartitionedVector, covariance: PartitionedPSDMatrix)(implicit ev: Field[PartitionedVector])

Value Members

final def !=(arg0: Any): Boolean

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

Definition Classes
AnyRef → Any
def *(other: PartitionedVector)(implicit ev: Field[PartitionedVector]): ContinuousRandomVariable[PartitionedVector]

Return the random variable that results from multiplying a provided quantity in the Domain to the current random variable.
Return the random variable that results from multiplying a provided quantity in the Domain to the current random variable.
other
The value to be multiplied to this random variable.
ev
An implicit parameter, which represents a Field defined over the set Domain.

Definition Classes
ContinuousRandomVariable
def *(other: ContinuousRandomVariable[PartitionedVector])(implicit ev: Field[PartitionedVector]): ContinuousRandomVariable[PartitionedVector]

Return the random variable that results from multiplying a provided random variable to the current random variable.
Return the random variable that results from multiplying a provided random variable to the current random variable.
other
The random variable to be multiplied to this.
ev
An implicit parameter, which represents a Field defined over the set Domain.

Definition Classes
ContinuousRandomVariable
def +(other: PartitionedVector)(implicit ev: Field[PartitionedVector]): ContinuousRandomVariable[PartitionedVector]

Return the random variable that results from adding a provided quantity in the Domain to the current random variable.
Return the random variable that results from adding a provided quantity in the Domain to the current random variable.
other
The value to be added to this random variable.
ev
An implicit parameter, which represents a Field defined over the set Domain.

Definition Classes
ContinuousRandomVariable
def +(other: ContinuousRandomVariable[PartitionedVector])(implicit ev: Field[PartitionedVector]): ContinuousRandomVariable[PartitionedVector]

Return the random variable that results from adding a provided random variable to the current random variable.
Return the random variable that results from adding a provided random variable to the current random variable.
other
The random variable to be added to this.

Definition Classes
ContinuousRandomVariable
def -(other: PartitionedVector)(implicit ev: Field[PartitionedVector]): ContinuousRandomVariable[PartitionedVector]

Return the random variable that results from subtracting a provided quantity in the Domain from the current random variable.
Return the random variable that results from subtracting a provided quantity in the Domain from the current random variable.
other
The value to be subtracted from this random variable.
ev
An implicit parameter, which represents a Field defined over the set Domain.

Definition Classes
ContinuousRandomVariable
def -(other: ContinuousRandomVariable[PartitionedVector])(implicit ev: Field[PartitionedVector]): ContinuousRandomVariable[PartitionedVector]

Return the random variable that results from subtracting a provided random variable from the current random variable.
Return the random variable that results from subtracting a provided random variable from the current random variable.
other
The random variable to be subtracted from this.
ev
An implicit parameter, which represents a Field defined over the set Domain.

Definition Classes
ContinuousRandomVariable
def :*[OtherDomain, OtherDistr <: ContinuousDistr[OtherDomain]](other: ContinuousRVWithDistr[OtherDomain, OtherDistr]): ContinuousRVWithDistr[(PartitionedVector, OtherDomain), ContinuousDistr[(PartitionedVector, OtherDomain)]]

Definition Classes
ContinuousRVWithDistr
def :*[Domain1, Dist1 <: Density[Domain1] with Rand[Domain1]](other: RandomVarWithDistr[Domain1, Dist1]): RandomVarWithDistr[(PartitionedVector, Domain1), GenericDistribution[(PartitionedVector, Domain1)]]

Cartesian product with another random variables which has a defined probability distribution.
Cartesian product with another random variables which has a defined probability distribution.

Definition Classes
RandomVarWithDistr
def :*[Domain1](other: RandomVariable[Domain1]): RandomVariable[(PartitionedVector, Domain1)]

Outputs the cartesian product between two random variables.
Outputs the cartesian product between two random variables.
Domain1
The domain of the other random variable
other
The random variable which forms the second component of the cartesian product.

Definition Classes
RandomVariable
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def >[OtherDomain](transformation: DataPipe[PartitionedVector, OtherDomain]): MeasurableFunction[PartitionedVector, OtherDomain, RandomVariable[PartitionedVector]]

Transform the current random variable on Domain to a morphed random variable on OtherDomain
Transform the current random variable on Domain to a morphed random variable on OtherDomain

Definition Classes
RandomVariable
final def asInstanceOf[T0]: T0

Definition Classes
Any
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
val covariance: PartitionedPSDMatrix
def draw: PartitionedVector

Alias for sample.run()
Alias for sample.run()

Definition Classes
RandomVariable
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
def iid(n: Int): IIDContinuousRVDistr[PartitionedVector, BlockedMultivariateStudentsT, MultStudentsTPRV.this.type]

Create an iid random variable from the current (this)
Create an iid random variable from the current (this)
n
The number of iid samples of the base random variable.

Definition Classes
ContinuousRVWithDistr → RandomVarWithDistr → RandomVariable
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
val mean: PartitionedVector
val mu: Double
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
val sample: DataPipe[Unit, PartitionedVector]

Generate a sample from the random variable
Generate a sample from the random variable

Definition Classes
ContinuousRVWithDistr → RandomVariable
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
val underlyingDist: BlockedMultivariateStudentsT

The actual probability density function is represented as a breeze Density object.
The actual probability density function is represented as a breeze Density object.

Definition Classes
MultStudentsTPRV → ContinuousRVWithDistr → RandomVarWithDistr → HasDistribution
final def wait(): Unit

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

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

Definition Classes
AnyRef
Annotations
@throws( ... )

Related Docs: object MultStudentsTPRV | package probability

case class MultStudentsTPRV(mu: Double, mean: PartitionedVector, covariance: PartitionedPSDMatrix)(implicit ev: Field[PartitionedVector]) extends AbstractStudentsTRandVar[PartitionedVector, PartitionedPSDMatrix, BlockedMultivariateStudentsT] with Product with Serializable

Instance Constructors

new MultStudentsTPRV(mu: Double, mean: PartitionedVector, covariance: PartitionedPSDMatrix)(implicit ev: Field[PartitionedVector])

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

def *(other: PartitionedVector)(implicit ev: Field[PartitionedVector]): ContinuousRandomVariable[PartitionedVector]

def *(other: ContinuousRandomVariable[PartitionedVector])(implicit ev: Field[PartitionedVector]): ContinuousRandomVariable[PartitionedVector]

def +(other: PartitionedVector)(implicit ev: Field[PartitionedVector]): ContinuousRandomVariable[PartitionedVector]

def +(other: ContinuousRandomVariable[PartitionedVector])(implicit ev: Field[PartitionedVector]): ContinuousRandomVariable[PartitionedVector]

def -(other: PartitionedVector)(implicit ev: Field[PartitionedVector]): ContinuousRandomVariable[PartitionedVector]

def -(other: ContinuousRandomVariable[PartitionedVector])(implicit ev: Field[PartitionedVector]): ContinuousRandomVariable[PartitionedVector]

def :*[OtherDomain, OtherDistr <: ContinuousDistr[OtherDomain]](other: ContinuousRVWithDistr[OtherDomain, OtherDistr]): ContinuousRVWithDistr[(PartitionedVector, OtherDomain), ContinuousDistr[(PartitionedVector, OtherDomain)]]

def :*[Domain1, Dist1 <: Density[Domain1] with Rand[Domain1]](other: RandomVarWithDistr[Domain1, Dist1]): RandomVarWithDistr[(PartitionedVector, Domain1), GenericDistribution[(PartitionedVector, Domain1)]]

def :*[Domain1](other: RandomVariable[Domain1]): RandomVariable[(PartitionedVector, Domain1)]

final def ==(arg0: Any): Boolean

def >[OtherDomain](transformation: DataPipe[PartitionedVector, OtherDomain]): MeasurableFunction[PartitionedVector, OtherDomain, RandomVariable[PartitionedVector]]

final def asInstanceOf[T0]: T0

def clone(): AnyRef

val covariance: PartitionedPSDMatrix

def draw: PartitionedVector

final def eq(arg0: AnyRef): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def iid(n: Int): IIDContinuousRVDistr[PartitionedVector, BlockedMultivariateStudentsT, MultStudentsTPRV.this.type]

final def isInstanceOf[T0]: Boolean

val mean: PartitionedVector

val mu: Double

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

val sample: DataPipe[Unit, PartitionedVector]

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

val underlyingDist: BlockedMultivariateStudentsT

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from Product

Inherited from Equals

Inherited from AbstractStudentsTRandVar[PartitionedVector, PartitionedPSDMatrix, BlockedMultivariateStudentsT]

Inherited from ContinuousRVWithDistr[PartitionedVector, BlockedMultivariateStudentsT]

Inherited from RandomVarWithDistr[PartitionedVector, BlockedMultivariateStudentsT]

Inherited from HasDistribution[PartitionedVector]

Inherited from ContinuousRandomVariable[PartitionedVector]

Inherited from RandomVariable[PartitionedVector]

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped