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 FilterArray
6.  object GibbsSampling extends App
7.  object GibbsWishart

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

8.  object KalmanFilter
9.  object Metropolis
10.  object MetropolisHastings
11.  object ParticleFilter

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

12.  object ParticleGibbs

    Particle Gibbs Sampler for A Dynamic Generalised Linear Model

13.  object ParticleGibbsAncestor extends App

    Particle Gibbs with Ancestor Sampling Requires a Tractable state evolution kernel

14.  object Smoothing
15.  object Streaming

    Utility class for parallelism and IO

16.  object StudentT
17.  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

18.  object SvdSampler

    Backward Sampler utilising the SVD for stability TODO: Check this