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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 backwardStepOu(p: SvParameters)(kfState: KfState, s: SamplingState): SamplingState

   Perform a backward step of FFBS for the OU process

6.  def backwardsSmoother(mod: Dlm)(kfState: Vector[KfState]): Vector[SmoothingState]

   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

7.  def clone(): AnyRef

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8.  final def eq(arg0: AnyRef): Boolean

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9.  def equals(arg0: Any): Boolean

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10.  def ffbs(mod: Dlm, observations: Vector[Data], advState: (KfState, Double) ⇒ KfState, backStep: (KfState, SamplingState) ⇒ SamplingState, p: DlmParameters): Rand[Vector[SamplingState]]

    Forward filtering backward sampling

    Forward filtering backward sampling

    mod

    the DLM

    observations

    a list of observations

    advState

    the a priori state update in a Kalman Filter

    backStep

    the backward step of the backward sampler

    p

    parametes of the DLM

11.  def ffbsDlm(mod: Dlm, ys: Vector[Data], p: DlmParameters): Rand[Vector[SamplingState]]

    Forward filtering backward sampling for a DLM

    Forward filtering backward sampling for a DLM

    mod

    the DLM

    p

    parametes of the DLM

12.  def finalize(): Unit

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13.  final def getClass(): Class[_]

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14.  def hashCode(): Int

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15.  def initialise(filtered: Vector[KfState]): SamplingState
16.  final def isInstanceOf[T0]: Boolean

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17.  final def ne(arg0: AnyRef): Boolean

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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(mod: Dlm, filtered: Vector[KfState], backStep: (KfState, SamplingState) ⇒ SamplingState): Vector[SamplingState]

    Perform backward sampling

21.  def sampleArray[FS, S, M](filtered: Array[FS], initialise: S, backStep: (FS, S) ⇒ S)(implicit ct: ClassTag[S]): Array[S]

    Perform backward sampling using a cfor loop

22.  def sampleDlm(mod: Dlm, filtered: Vector[KfState], w: DenseMatrix[Double]): Vector[SamplingState]

    Perform backward sampling for a DLM

23.  def smoothStep(mod: Dlm)(kfState: KfState, state: SmoothingState): SmoothingState

    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

24.  def step(mod: Dlm, w: DenseMatrix[Double])(kfState: KfState, state: SamplingState): SamplingState

    Perform a single step of the Backward Sampling algorithm for a DLM

    Perform a single step of the Backward Sampling algorithm for a DLM

    mod

    a DLM

    w

    the system noise covariance matrix

    kfState

    the state at the Kalman Filter at time t

    state

    the sampling state at time t + 1

    returns

    a sample from the state

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

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26.  def toString(): String

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27.  final def wait(): Unit

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

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29.  final def wait(arg0: Long): Unit

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