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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 clone(): AnyRef

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

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7.  def equals(arg0: Any): Boolean

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8.  def filter(mod: Dlm, variances: Vector[Double], observations: Vector[Data], params: DlmParameters): Vector[KfState]
9.  def finalize(): Unit

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10.  final def getClass(): Class[_]

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11.  def hashCode(): Int

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12.  def interpolate(data: Vector[Data], mod: Dglm, nu: Int, p: DlmParameters): Process[State]
13.  def interpolateStep(data: Vector[Data], mod: Dglm, p: DlmParameters): (State) ⇒ Rand[State]

    Sample the latent state for the student's t model

14.  final def isInstanceOf[T0]: Boolean

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15.  def ll(ys: Vector[Data], xs: Vector[SamplingState], mod: Dglm, p: DlmParameters)(nu: Int): Double

    Calculate the log-likelihood of the student's t-distributed model

    Calculate the log-likelihood of the student's t-distributed model

    ys

    a vector of observations

    xs

    a sample from the latent-state of the student's t model

    mod

    the Student t DGLM

    p

    the

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

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17.  final def notify(): Unit

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18.  final def notifyAll(): Unit

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19.  def sample(data: Vector[Data], priorW: InverseGamma, priorNu: DiscreteDistr[Int], propNu: (Int) ⇒ Rand[Int], propNuP: (Int, Int) ⇒ Double, mod: Dglm, params: DlmParameters): Process[State]

    Perform Gibbs Sampling for the Student t-distributed model

    Perform Gibbs Sampling for the Student t-distributed model

    priorW

    the prior distribution of the system noise matrix

    mod

    the DGLM representing the Student's t model

    params

    the initial parameters

20.  def sampleNu(prop: (Int) ⇒ Rand[Int], propP: (Int, Int) ⇒ Double, prior: (Int) ⇒ Double, likelihood: (Int) ⇒ Double): (Int) ⇒ Rand[(Int, Boolean)]

    Sample the degrees of freedom for the observation distribution

21.  def samplePmmh(data: Vector[Data], priorW: ContinuousDistr[Double], priorV: ContinuousDistr[Double], priorNu: DiscreteDistr[Int], prop: (DlmParameters) ⇒ Rand[DlmParameters], propNu: (Int) ⇒ Rand[Int], propNuP: (Int, Int) ⇒ Double, model: Dlm, n: Int, initP: DlmParameters, initNu: Int): Process[PmmhState]

    Particle Marginal Metropolis Hastings for the Student's t-distributed state space model

22.  def sampleScaleT(dof: Int, variances: Vector[Double]): Gamma

    Sample the (square of the) scale of the Student's t distribution

    Sample the (square of the) scale of the Student's t distribution

    dof

    the degrees of freedom of the Student's t observation distribution

23.  def sampleState(variances: Vector[Double], mod: Dlm, observations: Vector[Data], params: DlmParameters): Rand[Vector[SamplingState]]

    Sample the state, incorporating the drawn variances for each observation

    Sample the state, incorporating the drawn variances for each observation

    variances

    the sampled auxiliary parameters

    mod

    the DLM

    params

    the parameters of the DLM model

24.  def sampleVariances(ys: Vector[Data], f: (Double) ⇒ DenseMatrix[Double], dof: Int, theta: Vector[SamplingState], p: DlmParameters): Vector[Double]

    Sample the variances of the Normal distribution These are auxilliary variables required when calculating the one-step prediction in the Kalman Filter

    Sample the variances of the Normal distribution These are auxilliary variables required when calculating the one-step prediction in the Kalman Filter

    ys

    an array of observations of length N

    f

    the observation matrix

    dof

    the degrees of freedom of the Student's t-distribution

    returns

    a Rand distribution over the list of N variances

25.  def step(data: Vector[Data], priorW: InverseGamma, priorNu: DiscreteDistr[Int], propNu: (Int) ⇒ Rand[Int], propNuP: (Int, Int) ⇒ Double, mod: Dglm, p: DlmParameters): (State) ⇒ Rand[State]

    A single step of the Student t-distribution Gibbs Sampler

26.  def stepPmmh(priorW: ContinuousDistr[Double], priorV: ContinuousDistr[Double], priorNu: DiscreteDistr[Int], prop: (DlmParameters) ⇒ Rand[DlmParameters], propNu: (Int) ⇒ Rand[Int], propNuP: (Int, Int) ⇒ Double, ll: (DlmParameters, Int) ⇒ Double): (PmmhState) ⇒ Rand[PmmhState]

    Perform a single step of the PMMH algorithm for the Student's t distributed state space model

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

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28.  def toString(): String

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29.  final def wait(): Unit

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

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

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