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
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
case class MultivariateGaussianSvd(mu: DenseVector[Double], cov: DenseMatrix[Double])(implicit rand: RandBasis = Rand) extends ContinuousDistr[DenseVector[Double]] with Product with Serializable
case class ScaledStudentsT(dof: Double, location: Double, scale: Double)(implicit rand: RandBasis = Rand) extends ContinuousDistr[Double] with Moments[Double, Double] with Product with Serializable
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
object Dlm
object FilterArray
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 Dynamic Generalised Linear Models (DGLMs), 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 StudentT
object SvdFilter

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
object SvdSampler

Backward Sampler utilising the SVD for stability TODO: Check this
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]