model

type ConditionalLl = (Observation, DenseVector[Double]) ⇒ Double
case class Data(time: Time, observation: Option[Observation]) extends Product with Serializable

A single observation of a model
case class InverseGamma(shape: Double, scale: Double)(implicit rand: RandBasis = Rand) extends ContinuousDistr[Double] with Moments[Double, Double] with Product with Serializable
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
type LatentState = List[(Time, DenseVector[Double])]
case class MultivariateGaussianSvd(mu: DenseVector[Double], cov: DenseMatrix[Double])(implicit rand: RandBasis = Rand) extends ContinuousDistr[DenseVector[Double]] with Product with Serializable
type Observation = DenseVector[Double]
type ObservationMatrix = (Time) ⇒ DenseMatrix[Double]
type SystemMatrix = (Time) ⇒ DenseMatrix[Double]
type Time = Int
trait TimeSeries[F[_]] extends Monad[F] with Traverse[F]
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

object Dglm

DGLM
object Dlm
object GibbsSampling extends App
object GibbsWishart

This class learns a correlated system matrix using the InverseWishart prior on the system noise matrix
object KalmanFilter
object Metropolis
object MetropolisHastings
object ParticleFilter

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

Particle Gibbs Sampler for A Dynamic Generalised Linear Model
object ParticleGibbsAncestor extends App

Particle Gibbs with Ancestor Sampling Requires a Tractable state evolution kernel
object Smoothing
object Streaming

Utility class for parallelism and IO
object TimeSeries extends Serializable
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
implicit val randMonad: Monad[Rand]