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core.dlm.model

FilterArray

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object FilterArray

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  1. final def !=(arg0: Any): Boolean

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  2. final def ##(): Int

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  8. def ffbsNaive(mod: Model, ys: Vector[Data], p: Parameters): Rand[Array[(Double, DenseVector[Double])]]

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  9. def ffbsSvd(mod: Model, ys: Vector[Data], p: Parameters): Rand[Array[(Double, DenseVector[Double])]]

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    Forward filtering backward sampling by updating the array in place

  10. def filter[S](ys: Vector[Data], step: (S, Data) ⇒ S, initialise: S, st: Array[S])(implicit arg0: ClassTag[S]): Array[S]

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    Perform the Kalman Filter using a cfor loop to be used in the Gibbs Sampler

  11. def filterNaive(mod: Model, ys: Vector[Data], p: Parameters): Array[State]

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  12. def filterSvd(mod: Model, ys: Vector[Data], p: Parameters): Array[State]

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  13. def finalize(): Unit

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  18. final def notify(): Unit

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  19. final def notifyAll(): Unit

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  20. def sample[KS, S](filtered: Array[KS], step: (KS, S) ⇒ S, initialise: S, st: Array[S])(implicit arg0: ClassTag[S]): Array[S]

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    Perform backward sampling

  21. def sampleNaive(mod: Model, w: DenseMatrix[Double], filtered: Array[State]): Array[SamplingState]

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  22. def sampleSvd(mod: Model, w: DenseMatrix[Double], filtered: Array[State]): Array[State]

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  23. final def synchronized[T0](arg0: ⇒ T0): T0

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