StudentTGibbs

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. final def asInstanceOf[T0]: T0

   

   Definition Classes

   Any

5. def clone(): AnyRef

   

   Attributes

   protected[java.lang]

   Definition Classes

   AnyRef

   Annotations

   @throws( ... )

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

   

   Definition Classes

   AnyRef

7. def equals(arg0: Any): Boolean

   

   Definition Classes

   AnyRef → Any

8. def finalize(): Unit

   

   Attributes

   protected[java.lang]

   Definition Classes

   AnyRef

   Annotations

   @throws( classOf[java.lang.Throwable] )

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

   

   Definition Classes

   AnyRef → Any

10. def hashCode(): Int

    

    Definition Classes

    AnyRef → Any

11. final def isInstanceOf[T0]: Boolean

    

    Definition Classes

    Any

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

    

    Definition Classes

    AnyRef

13. final def notify(): Unit

    

    Definition Classes

    AnyRef

14. final def notifyAll(): Unit

    

    Definition Classes

    AnyRef

15. def sample(dof: Int, data: Vector[Data], priorW: InverseGamma, mod: Model, params: Parameters): Process[State]

    

    Perform Gibbs Sampling for the Student t-distributed model

16. def sampleScaleT(variances: Vector[Double], dof: Int): Rand[Double]

    

    Sample the (square of the) scale of the Student's t distribution

17. def sampleState(variances: Vector[Double], params: Parameters, mod: Model, observations: Vector[Data]): Rand[Vector[(Double, DenseVector[Double])]]

    

    Sample the state, incorporating the drawn variances for each observation

18. def sampleVariances(ys: Vector[Data], f: (Double) ⇒ DenseMatrix[Double], state: Vector[(Double, DenseVector[Double])], dof: Int, scale: Double): Rand[Vector[Double]]

    

    Sample the variances of the Normal distribution These are auxilliary variables required when calculating the one-step prediction in the Kalman Filter

    Sample the variances of the Normal distribution These are auxilliary variables required when calculating the one-step prediction in the Kalman Filter

    ys

    an array of observations of length N

    f

    the observation matrix

    state

    a sample from the state, using sampleState

    dof

    the degrees of freedom of the Student's t-distribution

    scale

    the (square of the) scale of the Student's t-distribution

    returns

    a Rand distribution over the list of N variances

19. def step(dof: Int, data: Vector[Data], priorW: InverseGamma, mod: Model)(state: State): Rand[State]

    

    A single step of the Student t-distribution Gibbs Sampler

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

    

    Definition Classes

    AnyRef

21. def toString(): String

    

    Definition Classes

    AnyRef → Any

22. final def wait(): Unit

    

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )