o

dlm.core.model

StochasticVolatilityKnots

object StochasticVolatilityKnots

Use a Gaussian approximation to the state space to sample the stochastic volatility model with discrete regular observations and an AR(1) latent state

Linear Supertypes
AnyRef, Any
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. StochasticVolatilityKnots
  2. AnyRef
  3. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

Type Members

  1. type ConditionalFFBS = (SampleState, SampleState, SvParameters, Vector[(Double, Option[Double])]) ⇒ Rand[Vector[SampleState]]
  2. type ConditionalFilter = (SampleState, SvParameters, Vector[(Double, Option[Double])]) ⇒ Rand[Vector[SampleState]]
  3. type ConditionalSample = (SampleState, SvParameters, Vector[(Double, Option[Double])]) ⇒ Rand[Vector[SampleState]]
  4. case class OuSvState(params: SvParameters, alphas: Vector[SampleState], accepted: DenseVector[Int]) 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 approxLl(state: Vector[Double], ys: Vector[(Double, Option[Double])]): Double

    The log likelihood for the Gaussian approximation

    The log likelihood for the Gaussian approximation

    state

    the proposed state for the current block

    returns

    the log likelihood of the Gaussian approximation

  5. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  6. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  7. def discreteUniform(min: Int, max: Int): Int
  8. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  9. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  10. def exactLl(state: Vector[Double], ys: Vector[(Double, Option[Double])]): Double

    The exact log likelihood of the observations

    The exact log likelihood of the observations

    state

    the proposed state for the current block

    ys

    to observations for the current block

    returns

    The exact log likelihood of the observations

  11. def ffbsAr: (SampleState, SampleState, SvParameters, Vector[(Double, Option[Double])]) ⇒ Rand[Vector[SampleState]]
  12. def ffbsOu: (SampleState, SampleState, SvParameters, Vector[(Double, Option[Double])]) ⇒ Rand[Vector[SampleState]]
  13. def filterAr(start: SampleState, p: SvParameters, transObs: Vector[(Double, Option[Double])]): Rand[Vector[SampleState]]
  14. def filterOu(start: SampleState, p: SvParameters, transObs: Vector[(Double, Option[Double])]): Rand[Vector[SampleState]]
  15. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  16. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  17. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  18. def initialStateAr(p: SvParameters, ys: Vector[(Double, Option[Double])]): Rand[Vector[SampleState]]
  19. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  20. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  21. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  22. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  23. def sampleAr(end: SampleState, p: SvParameters, transObs: Vector[(Double, Option[Double])]): Rand[Vector[SampleState]]
  24. def sampleArBeta(priorPhi: Beta, priorMu: Gaussian, priorSigmaEta: InverseGamma, ys: Vector[(Double, Option[Double])], initP: SvParameters): Process[StochVolState]
  25. def sampleBlock(ys: Vector[(Double, Option[Double])], p: SvParameters, filter: ConditionalFFBS): (Vector[SampleState]) ⇒ Rand[(Vector[SampleState], Int)]
  26. def sampleEnd(ys: Vector[(Double, Option[Double])], p: SvParameters, filter: ConditionalFilter): (Vector[SampleState]) ⇒ Rand[(Vector[SampleState], Int)]
  27. def sampleKnots(min: Int, max: Int, n: Int): Rand[Vector[Int]]

    Sample knot positions by sampling block size from a uniform distribution between min and max for a sequence of observations of length n

    Sample knot positions by sampling block size from a uniform distribution between min and max for a sequence of observations of length n

    min

    the minimum size of a block

    max

    the maxiumum size of a block

    n

    the length of the observations

  28. def sampleOu(priorPhi: ContinuousDistr[Double], priorMu: ContinuousDistr[Double], priorSigma: ContinuousDistr[Double], ys: Vector[(Double, Option[Double])], initP: SvParameters): Process[OuSvState]
  29. def sampleOu(end: SampleState, p: SvParameters, transObs: Vector[(Double, Option[Double])]): Rand[Vector[SampleState]]
  30. def sampleParametersAr(priorPhi: Gaussian, priorMu: Gaussian, priorSigmaEta: InverseGamma, ys: Vector[(Double, Option[Double])]): Process[StochVolState]
  31. def samplePhiConjugate(prior: Gaussian, p: SvParameters, alphas: Vector[Double]): Rand[Double]

    Sample phi from the autoregressive state space from a conjugate Gaussian distribution

    Sample phi from the autoregressive state space from a conjugate Gaussian distribution

    prior

    a Gaussian prior distribution

    returns

    a function from the current state to the next state with a new value for phi sample from a Gaussian posterior distribution

  32. def sampleSigmaMetrop(prior: InverseGamma, p: SvParameters, alphas: Vector[Double]): (Double) ⇒ Rand[(Double, Int)]
  33. def sampleStart(ys: Vector[(Double, Option[Double])], p: SvParameters, sampler: ConditionalSample): (Vector[SampleState]) ⇒ Rand[(Vector[SampleState], Int)]
  34. def sampleStarts(min: Int, max: Int, length: Int): Vector[Int]
  35. def sampleState(ffbs: ConditionalFFBS, filter: ConditionalFilter, sampler: ConditionalSample)(ys: Vector[(Double, Option[Double])], p: SvParameters, knots: Vector[Int], state: Array[SampleState]): Array[SampleState]
  36. def sampleStateFold(ffbs: ConditionalFFBS, filter: ConditionalFilter, sampler: ConditionalSample)(ys: Vector[(Double, Option[Double])], p: SvParameters, knots: Vector[Int], state: Array[SampleState]): Vector[SampleState]
  37. def sampleStepAr(priorPhi: Gaussian, priorMu: Gaussian, priorSigmaEta: InverseGamma, ys: Vector[(Double, Option[Double])]): (StochVolState) ⇒ Rand[StochVolState]
  38. def sampleStepArBeta(priorPhi: Beta, priorMu: Gaussian, priorSigmaEta: InverseGamma, ys: Vector[(Double, Option[Double])]): (StochVolState) ⇒ Rand[StochVolState]
  39. def sampleStepOu(priorPhi: ContinuousDistr[Double], priorMu: ContinuousDistr[Double], priorSigma: ContinuousDistr[Double], ys: Vector[(Double, Option[Double])]): (OuSvState) ⇒ Rand[OuSvState]
  40. def sampleTau(prior: Gamma, p: SvParameters, alphas: Vector[Double]): Gamma

    Sample the precision of the AR(1) process

  41. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  42. def toString(): String
    Definition Classes
    AnyRef → Any
  43. def transformObs(ys: Vector[(Double, Option[Double])]): Vector[(Double, Option[Double])]
  44. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  45. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  46. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )

Inherited from AnyRef

Inherited from Any

Ungrouped