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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.  def arLikelihood(alphas: Vector[Double], p: SvParameters): Double

   Log-Likelihood of the AR(1) process

   Log-Likelihood of the AR(1) process

   p

   the current value of the parameters

   returns

   the log-likelihood

5.  final def asInstanceOf[T0]: T0

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6.  def clone(): AnyRef

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

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8.  def equals(arg0: Any): Boolean

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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 initialStateOu(p: SvParameters, ys: Vector[(Double, Option[Double])]): Rand[Vector[SampleState]]
13.  final def isInstanceOf[T0]: Boolean

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

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15.  final def notify(): Unit

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16.  final def notifyAll(): Unit

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17.  def observation(at: Double): Rand[Double]

    The observation function for the stochastic volatility model

18.  def ouLikelihood(p: SvParameters, alphas: Vector[(Double, Double)]): Double

    Marginal log likelihood of the OU process used to perform the Metropolis Hastings steps to learn the static parameters

    Marginal log likelihood of the OU process used to perform the Metropolis Hastings steps to learn the static parameters

    returns

    the log likelihood of the state given the static parameter values

19.  def sampleBeta(priorPhi: ContinuousDistr[Double], priorMu: Gaussian, priorSigma: InverseGamma, ys: Vector[(Double, Option[Double])]): Process[StochVolState]
20.  def sampleKt(ys: Vector[Option[Double]], alphas: Vector[Double]): Vector[Int]

    Sample the indices for the mixture model

    Sample the indices for the mixture model

    ys

    a collection of observations

    alphas

    the latent log-volatility

21.  def sampleMu(prior: Gaussian, p: SvParameters, alphas: Vector[Double]): Rand[Double]

    Sample mu from the autoregressive state space, from a Gaussian distribution

    Sample mu from the autoregressive state space, from a Gaussian distribution

    prior

    a Gaussian prior for the parameter

    returns

    a function from the current state of the Markov chain to a new state with a new mu sampled from the Gaussian posterior distribution

22.  def sampleMuOu(prior: ContinuousDistr[Double], delta: Double = 0.05, p: SvParameters, alphas: Vector[(Double, Double)]): (Double) ⇒ Rand[(Double, Int)]

    Sample the mean from the OU process using a metropolis step

    Sample the mean from the OU process using a metropolis step

    prior

    a prior distribution for the mean parameter, mu

    delta

    the standard deviation of the (Gaussian) proposal distribution

23.  def sampleOu(priorPhi: ContinuousDistr[Double], priorMu: ContinuousDistr[Double], priorSigma: InverseGamma, params: SvParameters, ys: Vector[(Double, Option[Double])]): Process[StochVolState]
24.  def samplePhi(prior: ContinuousDistr[Double], p: SvParameters, alpha: Vector[Double], tau: Double, lambda: Double): (Double) ⇒ Rand[Double]

    Sample Phi using a Beta proposal distribution

    Sample Phi using a Beta proposal distribution

    prior

    a prior distribution for the parameter phi

    tau

    a small tuning parameter for the beta proposal

    lambda

    a tuning parameter for the beta proposal distribution

    returns

    a Metropolis Hastings step sampling the value of Phi

25.  def samplePhiOu(prior: ContinuousDistr[Double], p: SvParameters, alphas: Vector[(Double, Double)], lambda: Double = 10.0, tau: Double = 0.05): (Double) ⇒ Rand[(Double, Int)]

    Sample the autoregressive parameter (between 0 and 1)

26.  def sampleSigma(prior: InverseGamma, p: SvParameters, alphas: Vector[Double]): Rand[Double]

    Sample sigma from the an inverse gamma distribution (sqrt)

    Sample sigma from the an inverse gamma distribution (sqrt)

    prior

    the prior for the variance of the noise of the latent-state

    p

    the current value of the parameters

    alphas

    the current value of the latent-state

    returns

    a distribution over the system variance

27.  def sampleSigmaMetropOu(prior: ContinuousDistr[Double], delta: Double = 0.05, p: SvParameters, alphas: Vector[(Double, Double)]): (Double) ⇒ Rand[(Double, Int)]

    Metropolis step to sample the value of sigma_eta

28.  def sampleStateAr(ys: Vector[(Double, Option[Double])], params: SvParameters, alphas: Vector[SampleState]): Rand[Vector[SampleState]]
29.  def sampleStateOu(ys: Vector[(Double, Option[Double])], params: SvParameters, alphas: Vector[SampleState]): Rand[Vector[SampleState]]
30.  def sampleTau(prior: Gamma, p: SvParameters, alphas: Vector[Double]): Gamma
31.  def sampleUni(priorPhi: Gaussian, priorMu: Gaussian, priorSigma: InverseGamma, ys: Vector[(Double, Option[Double])]): Process[StochVolState]
32.  def simOu(p: SvParameters, times: Stream[Double]): Stream[(Double, Option[Double], Double)]

    Simulate from a Stochastic Volatility model with Ornstein-Uhlenbeck latent State

33.  def simStep(time: Double, p: SvParameters)(state: Double): Rand[(Double, Option[Double], Double)]
34.  def simulate(p: SvParameters): Process[(Double, Option[Double], Double)]

    Simulate regularly from a stochastic volatility model with AR(1) latent-state

35.  def stepBeta(priorPhi: ContinuousDistr[Double], priorMu: Gaussian, priorSigma: InverseGamma, ys: Vector[(Double, Option[Double])]): (StochVolState) ⇒ Rand[StochVolState]
36.  def stepOu(priorPhi: ContinuousDistr[Double], priorMu: ContinuousDistr[Double], priorSigma: InverseGamma, ys: Vector[(Double, Option[Double])])(s: StochVolState): Rand[StochVolState]

    Perform a single step of the MCMC Kernel for the SV with OU latent state Using independent Metropolis-Hastings moves

37.  def stepOu(p: SvParameters, at: Double, dt: Double): ContinuousDistr[Double]

    Advance the state of the OU process

38.  def stepState(p: SvParameters, at: Double): ContinuousDistr[Double]
39.  def stepUni(priorPhi: Gaussian, priorMu: Gaussian, priorSigma: InverseGamma, ys: Vector[(Double, Option[Double])]): (StochVolState) ⇒ Rand[StochVolState]
40.  final def synchronized[T0](arg0: ⇒ T0): T0

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41.  def toString(): String

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42.  final def wait(): Unit

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

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

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