StudentT

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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

    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, variances: Vector[Double]): Gamma

    

    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

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 sampleVariances(ys: Vector[Data], f: (Double) ⇒ DenseMatrix[Double], dof: Int, theta: Vector[(Double, DenseVector[Double])], p: Parameters): 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

    dof

    the degrees of freedom 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, p: Parameters): (State) ⇒ Rand[State]

    

    A single step of the Student t-distribution Gibbs Sampler

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