object GibbsWishart
This class learns a correlated system matrix using the InverseWishart prior on the system noise matrix
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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
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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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def
wishartStep(mod: Model, priorV: InverseGamma, priorW: InverseWishart, observations: Vector[Data])(state: State): Rand[State]
A single step of the Gibbs Wishart algorithm