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dlm.core.model

GibbsSampling

object GibbsSampling

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  1. case class State(p: DlmParameters, state: Vector[SamplingState]) extends Product with Serializable

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  4. def arlikelihood(state: Vector[SamplingState], p: DlmParameters, phi: DenseVector[Double]): Double

    Calculate the marginal likelihood of phi given the values of the latent-state and other static parameters

    Calculate the marginal likelihood of phi given the values of the latent-state and other static parameters

    state

    a sample of the latent state of an AR(1) DLM

    p

    the static parameters of a DLM

    phi

    autoregressive

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  7. def dinvGammaStep(mod: Dlm, priorV: InverseGamma, priorW: InverseGamma, observations: Vector[Data]): (State) ⇒ Rand[State]

    A single step of a Gibbs Sampler

    A single step of a Gibbs Sampler

    mod

    the model containing the definition of the observation matrix F_t and system evolution matrix G_t

    priorV

    the prior distribution on the observation noise matrix, V

    priorW

    the prior distribution on the system noise matrix, W

    observations

    an array of Data containing the observed time series

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  11. final def getClass(): Class[_]
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  12. def gibbsMetropStep(proposal: (DlmParameters) ⇒ Rand[DlmParameters], mod: Dlm, priorV: InverseGamma, priorW: InverseGamma, observations: Vector[Data]): (State) ⇒ Rand[State]

    Use metropolis hastings to determine the initial state distribution x0 ~ N(m0, C0)

    Use metropolis hastings to determine the initial state distribution x0 ~ N(m0, C0)

    proposal

    a proposal distribution for the parameters of the initial state

    mod

    a DLM model specification

    priorV

    the prior distribution of the observation noise matrix

    priorW

    the prior distribution of the system noise matrix

    observations

    a vector of observations

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  15. def likelihood(theta: Vector[SamplingState], g: (Double) ⇒ DenseMatrix[Double])(p: DlmParameters): Double

    Calculate the marginal likelihood for metropolis hastings

  16. def metropSamples(proposal: (DlmParameters) ⇒ Rand[DlmParameters], mod: Dlm, priorV: InverseGamma, priorW: InverseGamma, initParams: DlmParameters, observations: Vector[Data]): Process[State]

    Use metropolis hastings to determine the initial state distribution x0 ~ N(m0, C0) And Gibbs sampling for the state and observation variance matrices

  17. def metropStep(mod: Dlm, theta: Vector[SamplingState], proposal: (DlmParameters) ⇒ Rand[DlmParameters]): (DlmParameters) ⇒ Rand[DlmParameters]

    A metropolis step for a DLM

    A metropolis step for a DLM

    mod

    a DLM model

    theta

    the currently sampled state of the DLM

    proposal

    a symmetric proposal distribution for the parameters of a DLM

    returns

    a function from Parameters => Rand[Parameters] which performs a metropolis step to be used in a Markov Chain

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  21. def sample(mod: Dlm, priorV: InverseGamma, priorW: InverseGamma, initParams: DlmParameters, observations: Vector[Data]): Process[State]

    Return a Markov chain using Gibbs Sampling to determine the values of the system and observation noise covariance matrices, W and V

    Return a Markov chain using Gibbs Sampling to determine the values of the system and observation noise covariance matrices, W and V

    mod

    the model containing the definition of the observation matrix F_t and system evolution matrix G_t

    priorV

    the prior distribution on the observation noise matrix, V

    priorW

    the prior distribution on the system noise matrix, W

    initParams

    the initial parameters of the Markov Chain

    observations

    an array of Data containing the observed time series

    returns

    a Process

  22. def sampleObservationMatrix(prior: InverseGamma, f: (Double) ⇒ DenseMatrix[Double], ys: Vector[DenseVector[Option[Double]]], theta: Vector[(Double, DenseVector[Double])]): Rand[DenseMatrix[Double]]

    Sample the (diagonal) observation noise covariance matrix from an Inverse Gamma distribution

    Sample the (diagonal) observation noise covariance matrix from an Inverse Gamma distribution

    prior

    an Inverse Gamma prior distribution for each variance element of the observation matrix

    ys

    the observed values of the time series

    theta

    a sample of the DLM state

    returns

    the posterior distribution over the diagonal observation matrix

  23. def samplePhi(prior: Beta, lambda: Double, tau: Double, s: State): (Double) ⇒ Rand[Double]

    Sample the autoregressive parameter with a Beta Prior and proposal distribution

  24. def sampleSvd(mod: Dlm, priorV: InverseGamma, priorW: InverseGamma, initParams: DlmParameters, observations: Vector[Data]): Process[State]

    Perform Gibbs Sampling using the SVD Kalman Filter for numerical stability

  25. def sampleSystemMatrix(prior: InverseGamma, theta: Vector[(Double, DenseVector[Double])], g: (Double) ⇒ DenseMatrix[Double]): Rand[DenseMatrix[Double]]

    Sample the diagonal system matrix for an irregularly observed DLM

  26. def stepSvd(mod: Dlm, priorV: InverseGamma, priorW: InverseGamma, observations: Vector[Data]): (State) ⇒ Rand[State]
  27. final def synchronized[T0](arg0: ⇒ T0): T0
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  28. def toString(): String
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  29. def updateModel(mod: Dlm, phi: Double*): Dlm

    Update an autoregressive model with a new value of the autoregressive parameter

  30. final def wait(): Unit
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