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dlm

model

package model

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Type Members

  1. case class InverseGamma (shape: Double, scale: Double)(implicit rand: RandBasis = Rand) extends ContinuousDistr[Double] with Moments[Double, Double] with Product with Serializable
  2. 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
  3. 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

  4. case class MultivariateGaussianSvd (mu: DenseVector[Double], cov: DenseMatrix[Double])(implicit rand: RandBasis = Rand) extends ContinuousDistr[DenseVector[Double]] with Product with Serializable
  5. case class ScaledStudentsT (dof: Double, location: Double, scale: Double)(implicit rand: RandBasis = Rand) extends ContinuousDistr[Double] with Moments[Double, Double] with Product with Serializable
  6. trait TimeSeries [F[_]] extends Monad[F] with Traverse[F]
  7. 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: Model): Model

    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 Dglm
  4. object Dlm
  5. object GibbsSampling extends App
  6. object GibbsWishart

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

  7. object KalmanFilter
  8. object Metropolis
  9. object MetropolisHastings
  10. object ParticleFilter

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

  11. object ParticleGibbs

    Particle Gibbs Sampler for A Dynamic Generalised Linear Model

  12. object ParticleGibbsAncestor extends App

    Particle Gibbs with Ancestor Sampling Requires a Tractable state evolution kernel

  13. object Smoothing
  14. object Streaming

    Utility class for parallelism and IO

  15. object StudentTGibbs
  16. object TimeSeries extends Serializable

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