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core.dlm.model

GibbsSampling

object GibbsSampling extends App

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App, DelayedInit, AnyRef, Any
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Type Members

  1. case class State(p: Parameters, state: Vector[(Double, DenseVector[Double])]) extends Product with Serializable

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. def args: Array[String]
    Attributes
    protected
    Definition Classes
    App
    Annotations
    @deprecatedOverriding( "args should not be overridden" , "2.11.0" )
  5. def arlikelihood(state: Vector[(Double, DenseVector[Double])], p: Parameters, 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

  6. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  7. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  8. def dinvGammaStep(mod: Model, 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

  9. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  10. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  11. val executionStart: Long
    Definition Classes
    App
    Annotations
    @deprecatedOverriding( ... , "2.11.0" )
  12. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  13. final def getClass(): Class[_]
    Definition Classes
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    Annotations
    @native()
  14. def gibbsMetropStep(proposal: (Parameters) ⇒ Rand[Parameters], mod: Model, 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

  15. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  16. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  17. def likelihood(theta: Vector[(Double, DenseVector[Double])], g: (Double) ⇒ DenseMatrix[Double])(p: Parameters): Double

    Calculate the marginal likelihood for metropolis hastings

  18. def main(args: Array[String]): Unit
    Definition Classes
    App
    Annotations
    @deprecatedOverriding( "main should not be overridden" , "2.11.0" )
  19. def metropSamples(proposal: (Parameters) ⇒ Rand[Parameters], mod: Model, priorV: InverseGamma, priorW: InverseGamma, initParams: Parameters, observations: Vector[Data]): Process[State]

  20. def metropStep(mod: Model, theta: Vector[(Double, DenseVector[Double])], proposal: (Parameters) ⇒ Rand[Parameters]): (Parameters) ⇒ Rand[Parameters]

    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

  21. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  22. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  23. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  24. def sample(mod: Model, priorV: InverseGamma, priorW: InverseGamma, initParams: Parameters, 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

  25. def sampleObservationMatrix(prior: InverseGamma, f: (Double) ⇒ DenseMatrix[Double], ys: Vector[Data], 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

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

    Sample the autoregressive parameter with a Beta Prior and proposal distribution

  27. 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

  28. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  29. def toString(): String
    Definition Classes
    AnyRef → Any
  30. def updateModel(mod: Model, phi: Double*): Model

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

  31. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  32. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  33. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
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    @native() @throws( ... )

Deprecated Value Members

  1. def delayedInit(body: ⇒ Unit): Unit
    Definition Classes
    App → DelayedInit
    Annotations
    @deprecated
    Deprecated

    (Since version 2.11.0) the delayedInit mechanism will disappear

Inherited from App

Inherited from DelayedInit

Inherited from AnyRef

Inherited from Any

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