Smoothing

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 backSampleStep(mod: Model, p: Parameters)(state: SamplingState, kfState: State): SamplingState

   

   Simulation version of the backwards smoother

6. def backSampleStepJoseph(mod: Model, p: Parameters)(state: SamplingState, kfState: State): SamplingState

   

   Backwards sample step from the distribution p(x_t | x_{t-1}, y_{1:t}), for use in the Gibbs Sampler This uses the joseph form of the covariance update for stability

7. def backwardSampling(mod: Model, kfState: Array[State], p: Parameters): Array[(Time, DenseVector[Double])]

   

8. def backwardsSmoother(mod: Model, p: Parameters)(kfState: Array[State]): Array[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

   p

   the parameters of the DLM

   kfState

   the output of a Kalman Filter

9. def clone(): AnyRef

   

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

    

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11. def equals(arg0: Any): Boolean

    

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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. final def isInstanceOf[T0]: Boolean

    

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16. def makeSymmetrix(m: DenseMatrix[Double]): DenseMatrix[Double]

    

    Copies the lower triangular portion of a matrix to the upper triangle

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 smoothStep(mod: Model, p: Parameters)(state: SmoothingState, kfState: State): 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

    p

    the parameters of the DLM

    state

    the state at time t + 1

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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