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dlm.core

model

package model

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Type Members

  1. case class AuxFilter(n: Int) extends FilterTs[PfState, DlmParameters, Dglm] with Product with Serializable

    Calculate an one-dimensional unknown observation variance

  2. case class ConjugateFilter(prior: InverseGamma, advState: (InverseGammaState, Double) ⇒ InverseGammaState) extends FilterTs[InverseGammaState, DlmParameters, Dlm] with Product with Serializable

    Calculate an one-dimensional unknown observation variance

  3. case class Data(time: Double, observation: DenseVector[Option[Double]]) extends Product with Serializable

    A single observation of a model

  4. case class Dglm(observation: (DenseVector[Double], DenseMatrix[Double]) ⇒ Rand[DenseVector[Double]], f: (Double) ⇒ DenseMatrix[Double], g: (Double) ⇒ DenseMatrix[Double], link: (DenseVector[Double]) ⇒ Double, conditionalLikelihood: (DenseMatrix[Double]) ⇒ (DenseVector[Double], DenseVector[Double]) ⇒ Double) extends Product with Serializable

    A DGLM used for modelling non-linear non-Gaussian univariate time series

  5. case class Dlm(f: (Double) ⇒ DenseMatrix[Double], g: (Double) ⇒ DenseMatrix[Double]) extends Product with Serializable

    A state space model with a linear Gaussian latent-state

    A state space model with a linear Gaussian latent-state

    f

    the observation matrix which can be a function of time

    g

    the system matrix

  6. case class DlmFsvParameters(dlm: DlmParameters, fsv: FsvParameters) extends Product with Serializable

    Parameters of a DLM with a Factor structure for the observation matrix

    Parameters of a DLM with a Factor structure for the observation matrix

    dlm

    the parameters of the (multivariate) DLM

    fsv

    the parameters of the

  7. case class DlmParameters(v: DenseMatrix[Double], w: DenseMatrix[Double], m0: DenseVector[Double], c0: DenseMatrix[Double]) extends Product with Serializable

    Parameters of a DLM

  8. trait FilterTs[S, P, M] extends AnyRef

    Abstract trait for a filter which allows the filter to be performed on any collection which implementes Traverse

  9. case class FsvParameters(v: DenseMatrix[Double], beta: DenseMatrix[Double], factorParams: Vector[SvParameters]) extends Product with Serializable

    Factor Stochastic Volatility Parameters for a model with k factors and p time series k << p

    Factor Stochastic Volatility Parameters for a model with k factors and p time series k << p

    v

    the variance of the measurement error

    beta

    the factor loading matrix, p x k

    factorParams

    a vector of length k containing the ISV parameters for the factors

  10. case class InverseGamma(shape: Double, scale: Double)(implicit rand: RandBasis = Rand) extends ContinuousDistr[Double] with Moments[Double, Double] with Product with Serializable
  11. case class InverseGammaState(kfState: KfState, variance: Vector[InverseGamma]) extends Product with Serializable

    State for the conjugate filter

    State for the conjugate filter

    kfState

    the latent-state

    variance

    the distribution of the observation precision

  12. case class InverseWishart(nu: Double, psi: DenseMatrix[Double])(implicit rand: RandBasis = Rand) extends ContinuousDistr[DenseMatrix[Double]] with Moments[DenseMatrix[Double], DenseMatrix[Double]] with Product with Serializable
  13. case class KalmanFilter(advState: (KfState, Double) ⇒ KfState) extends FilterTs[KfState, DlmParameters, Dlm] with Product with Serializable
  14. case class KfState(time: Double, mt: DenseVector[Double], ct: DenseMatrix[Double], at: DenseVector[Double], rt: DenseMatrix[Double], ft: Option[DenseVector[Double]], qt: Option[DenseMatrix[Double]]) extends Product with Serializable

    State for the Kalman Filter

    State for the Kalman Filter

    time

    the current timestep

    mt

    the posterior mean of the latent state

    ct

    the posterior covariance of the latent state

    at

    the prior mean of the latent state

    rt

    the prior covariance of the latent state

    qt

    the one step predicted observation covariance, not present at the first timestep

  15. case class LiuAndWestFilter(n: Int, prior: Rand[DlmParameters], a: Double, n0: Int) extends FilterTs[PfStateParams, DlmParameters, Dglm] with Product with Serializable

    Extended Particle filter which approximates the parameters as a particle cloud

  16. case class MatrixNormal(mu: DenseMatrix[Double], u: DenseMatrix[Double], v: DenseMatrix[Double])(implicit rand: RandBasis = Rand) extends ContinuousDistr[DenseMatrix[Double]] with Product with Serializable

    A Normal distribution over matrices

    A Normal distribution over matrices

    mu

    the location of the distribution

    u

    the variance of the rows

    v

    the variance of the columns

  17. case class MultivariateGaussianSvd(mu: DenseVector[Double], cov: DenseMatrix[Double])(implicit rand: RandBasis = Rand) extends ContinuousDistr[DenseVector[Double]] with Product with Serializable
  18. case class MultivariateStudentsT(dof: Double, location: DenseVector[Double], shape: DenseMatrix[Double])(implicit rand: RandBasis = Rand) extends ContinuousDistr[DenseVector[Double]] with Moments[DenseVector[Double], DenseMatrix[Double]] with Product with Serializable
  19. case class ParticleFilter(n: Int, n0: Int, resample: (Vector[DenseVector[Double]], Vector[Double]) ⇒ Vector[DenseVector[Double]]) extends FilterTs[PfState, DlmParameters, Dglm] with Product with Serializable

    A bootstrap particle filter which can be used for inference of Dynamic Generalised Linear Models (DGLMs), where the observation distribution is not Gaussian.

    A bootstrap particle filter which can be used for inference of Dynamic Generalised Linear Models (DGLMs), where the observation distribution is not Gaussian.

    n

    the number of particles used in the filter

    n0

    if ESS < n0 then resample

  20. case class ParticleGibbs(n: Int) extends Product with Serializable

    Particle Gibbs Sampler for A Dynamic Generalised Linear Dglm

  21. case class PfState(time: Double, state: Vector[DenseVector[Double]], weights: Vector[Double], ll: Double) extends Product with Serializable

    State of the Bootstrap Particle Filter

  22. case class PfStateParams(time: Double, state: Vector[DenseVector[Double]], weights: Vector[Double], params: Vector[DlmParameters]) extends Product with Serializable

    State for the particle filter with parameters TODO: Gamma mixture distribution

    State for the particle filter with parameters TODO: Gamma mixture distribution

    time

    the time of the observation

    state

    a particle cloud representing the latent-state

    weights

    the conditional log-likelihood of the latent-state

    params

    a particle cloud representing the values of the parameters

  23. case class PgState(conditionedState: Map[Double, DenseVector[Double]], states: Vector[Vector[(Double, DenseVector[Double])]], weights: Vector[Double], ll: Double) extends Product with Serializable
  24. case class RaoBlackwellFilter(n: Int, prior: Rand[DlmParameters], a: Double, n0: Int) extends FilterTs[RbState, DlmParameters, Dlm] with Product with Serializable

    Extended Particle filter which approximates the parameters as a particle cloud

  25. case class RbState(time: Double, params: Vector[DlmParameters], mt: Vector[DenseVector[Double]], ct: Vector[DenseMatrix[Double]], weights: Vector[Double]) extends Product with Serializable
  26. case class SamplingState(time: Double, sample: DenseVector[Double], mean: DenseVector[Double], cov: DenseMatrix[Double], at1: DenseVector[Double], rt1: DenseMatrix[Double]) extends Product with Serializable
  27. case class ScaledStudentsT(dof: Double, location: Double, scale: Double)(implicit rand: RandBasis = Rand) extends ContinuousDistr[Double] with Moments[Double, Double] with Product with Serializable
  28. trait Simulate[M, P, S] extends AnyRef

    Abstract trait to simulate data

  29. case class StochVolState(params: SvParameters, alphas: Vector[SampleState]) extends Product with Serializable
  30. case class StorvikState(time: Double, state: Vector[DenseVector[Double]], params: Vector[DlmParameters], statsV: Vector[Vector[InverseGamma]], statsW: Vector[Vector[InverseGamma]], ess: Int) extends Product with Serializable

    State of the Storvik filter

    State of the Storvik filter

    time

    the time of the observation associated with this latent state

    state

    the particle cloud representing the posterior state

  31. case class SvParameters(phi: Double, mu: Double, sigmaEta: Double) extends Product with Serializable
  32. case class SvdFilter(advState: (SvdState, Double) ⇒ SvdState) extends FilterTs[SvdState, DlmParameters, Dlm] with Product with Serializable

    Perform the Kalman Filter by updating the value of the Singular Value Decomp.

    Perform the Kalman Filter by updating the value of the Singular Value Decomp. of the state covariance matrix, C = UDU^T

    https://arxiv.org/pdf/1611.03686.pdf

  33. case class SvdState(time: Double, mt: DenseVector[Double], dc: DenseVector[Double], uc: DenseMatrix[Double], at: DenseVector[Double], dr: DenseVector[Double], ur: DenseMatrix[Double], ft: DenseVector[Double]) extends Product with Serializable
  34. case class Wishart(n: Double, scale: DenseMatrix[Double])(implicit rand: RandBasis = Rand) extends ContinuousDistr[DenseMatrix[Double]] with Moments[DenseMatrix[Double], DenseMatrix[Double]] with Product with Serializable

Value Members

  1. implicit def dlm2dglm(dlmModel: Dlm): Dglm

    A Gaussian DLM can be implicitly converted to a DGLM Then particle filtering methods can be used on Gaussian Models

  2. implicit val randMonad: Monad[Rand]
  3. object AuxFilter extends Serializable
  4. object ConjugateFilter extends Serializable
  5. object Dglm extends Serializable
  6. object Dlm extends Serializable

    A DLM with a p-vector of observations y_t = F_t x_t + v_t, v_t ~ N(0, V) x_t = F_t x_{t-1} + w_t, w_t ~ N(0, W)

  7. object DlmFsv

    Model a heteroskedastic time series DLM by modelling the log-covariance of the observation variance as latent-factors

  8. object DlmFsvParameters extends Serializable
  9. object DlmFsvSystem

    Fit a DLM with the system variance modelled using an FSV model and latent log volatility modelled using continuous time Ornstein-Uhlenbeck process

  10. object DlmParameters extends Serializable
  11. object FactorSv

    Model a large covariance matrix using a factor structure

  12. object FilterAr

    Filtering for the stochastic volatility model

  13. object FilterOu
  14. object FilterTs
  15. object FsvParameters extends Serializable
  16. object GibbsSampling
  17. object GibbsWishart

    This class learns a correlated system matrix using the InverseWishart prior on the system noise matrix

  18. object KalmanFilter extends Serializable
  19. object LiuAndWestFilter extends Serializable
  20. object Metropolis
  21. object MetropolisHastings
  22. object ParticleFilter extends Serializable
  23. object ParticleGibbs extends Serializable
  24. object Smoothing
  25. object StochasticVolatility

    Simulate and fit a Stochastic volatility model using a mixture model approximation for the non-linear dynamics for either a AR(1) latent-state or OU latent state

    Simulate and fit a Stochastic volatility model using a mixture model approximation for the non-linear dynamics for either a AR(1) latent-state or OU latent state

    Y_t = sigma * exp(a_t / 2), sigma ~ N(0, 1) a_t = phi * a_t + eta, eta ~ N(0, sigma_eta)

  26. 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

  27. object StorvikFilter
  28. object Streaming
  29. object StudentT

    Fit a state space model with a latent Gaussian state and a Student's t observation distribution using the fact that the student's t-distribution is an Inverse Gamma mixture of normals

  30. object SvParameters extends Serializable
  31. object SvdFilter extends Serializable
  32. object SvdSampler

    Backward Sampler utilising the SVD for stability TODO: Check this

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