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

   @native() @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

   Annotations

   @native()

10.  def hashCode(): Int

    Definition Classes

    AnyRef → Any

    Annotations

    @native()

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

    Annotations

    @native()

14.  final def notifyAll(): Unit

    Definition Classes

    AnyRef

    Annotations

    @native()

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

    Perform Gibbs Sampling for the Student t-distributed model

    dof

    the degrees of freedom for the Student's t distributed observation dist

    priorW

    the prior distribution of the system noise matrix

    mod

    the DGLM representing the Student's t model

    params

    the initial parameters

16.  def sampleScaleT(dof: Int)(s: State): Rand[State]

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

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

    dof

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

    s

    the current state of the MCMC algorithm

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

    Sample the state, incorporating the drawn variances for each observation

    Sample the state, incorporating the drawn variances for each observation

    mod

    the DLM

18.  def sampleSystemMatrix(mod: Model, priorW: InverseGamma)(s: State): Rand[State]
19.  def sampleVariances(ys: Vector[Data], f: (Double) ⇒ DenseMatrix[Double], dof: Int): (State) ⇒ Rand[State]

    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

    dof

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

    returns

    a Rand distribution over the list of N variances

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

    A single step of the Student t-distribution Gibbs Sampler

21.  def stepState(mod: Model, observations: Vector[Data])(s: State): Rand[State]
22.  final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes

    AnyRef

23.  def toString(): String

    Definition Classes

    AnyRef → Any

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

    @native() @throws( ... )