GibbsWishart

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15. def sample(mod: Model, priorV: InverseGamma, priorW: InverseWishart, initParams: Parameters, observations: Vector[Data]): Process[State]

    

    Perofrm Gibbs Sampling using an Inverse Wishart distribution for the system noise matrix

    Perofrm Gibbs Sampling using an Inverse Wishart distribution for the system noise matrix

    mod

    a DLM model specification

    priorV

    the prior on the observation noise matrix

    priorW

    the Inverse Wishart prior distribution on the system covariance matrix

    initParams

    the intial parameters of the Markov Chain

    observations

    a vector of time series observations

16. def sampleSystemMatrix(priorW: InverseWishart, g: (Double) ⇒ DenseMatrix[Double], theta: Vector[(Double, DenseVector[Double])]): InverseWishart

    

    Sample the system covariance matrix using an Inverse Wishart prior on the system covariance matrix

    Sample the system covariance matrix using an Inverse Wishart prior on the system covariance matrix

    priorW

    the prior distribution of the System evolution noise matrix

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22. def wishartStep(mod: Model, priorV: InverseGamma, priorW: InverseWishart, observations: Vector[Data]): (State) ⇒ Rand[State]

    

    A single step of the Gibbs Wishart algorithm