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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

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