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core.dlm.model

Smoothing

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object Smoothing

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  1. case class SamplingState(time: Double, sample: DenseVector[Double], at1: DenseVector[Double], rt1: DenseMatrix[Double]) extends Product with Serializable

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  2. case class SmoothingState(time: Double, mean: DenseVector[Double], covariance: DenseMatrix[Double], at1: DenseVector[Double], rt1: DenseMatrix[Double]) extends Product with Serializable

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Value Members

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

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  2. final def ##(): Int

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  3. final def ==(arg0: Any): Boolean

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  4. final def asInstanceOf[T0]: T0

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  5. def backwardsSmoother(mod: Model)(kfState: Vector[State]): Vector[SmoothingState]

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    Learn the distribution of the latent state (the smoothing distribution) p(x_{1:T} | y_{1:T}) of a fully specified DLM with observations available for all time

    Learn the distribution of the latent state (the smoothing distribution) p(x_{1:T} | y_{1:T}) of a fully specified DLM with observations available for all time

    mod

    a DLM model specification

    kfState

    the output of a Kalman Filter

  6. def clone(): AnyRef

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  7. final def eq(arg0: AnyRef): Boolean

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  8. def equals(arg0: Any): Boolean

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  9. def ffbs(mod: Model, observations: Vector[Data], p: Parameters): Rand[Vector[(Double, DenseVector[Double])]]

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    Forward filtering backward sampling for a DLM

    Forward filtering backward sampling for a DLM

    mod

    the DLM

    observations

    a list of observations

    p

    parametes of the DLM

  10. def finalize(): Unit

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  11. final def getClass(): Class[_]

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  12. def hashCode(): Int

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  13. def initialise(filtered: Vector[State]): SamplingState

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  14. final def isInstanceOf[T0]: Boolean

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  15. final def ne(arg0: AnyRef): Boolean

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  16. final def notify(): Unit

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  17. final def notifyAll(): Unit

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  18. def sample(mod: Model, filtered: Vector[State], w: DenseMatrix[Double]): Vector[(Double, DenseVector[Double])]

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  19. def smoothStep(mod: Model)(kfState: State, state: SmoothingState): SmoothingState

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    A single step in the backwards smoother Requires that a Kalman Filter has been run on the model

    A single step in the backwards smoother Requires that a Kalman Filter has been run on the model

    mod

    a DLM model specification

    state

    the state at time t + 1

  20. def step(mod: Model, w: DenseMatrix[Double])(kfState: State, state: SamplingState): SamplingState

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

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  22. def toString(): String

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  23. final def wait(): Unit

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  24. final def wait(arg0: Long, arg1: Int): Unit

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  25. final def wait(arg0: Long): Unit

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