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dlm.model

GibbsWishart

object GibbsWishart

This class learns a correlated system matrix using the InverseWishart prior on the system noise matrix

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

    Do some gibbs samples

    Do some gibbs samples

    mod

    a DLM model specification

    priorV

    the prior on the observation noise matrix

    priorW

    the prior distribution on the system covariance matrix

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

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

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

    A single step of the Gibbs Wishart algorithm

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