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dlm.model

ParticleGibbs

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object ParticleGibbs

Particle Gibbs Sampler for A Dynamic Generalised Linear Model

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  1. case class State(states: List[List[(Double, DenseVector[Double])]], weights: List[Double], ll: Double) extends Product with Serializable

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  8. def filter(n: Int, p: Parameters, mod: Model, obs: List[Data])(state: List[(Double, DenseVector[Double])]): Rand[(Double, List[(Double, DenseVector[Double])])]

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    Run the Particle Gibbs Filter, given a samples value of the state

    Run the Particle Gibbs Filter, given a samples value of the state

    n

    the number of particles in the filter

    p

    the parameters used to run the filter

    mod

    the specification of the system evolution matrix, G and observation matrix F

    obs

    a list of observations

    state

    the conditioned upon state

    returns

    a tuple containing the log-likelihood of the parameters given the observations and a single state path deterministically chosen to be the final path

  9. def finalize(): Unit

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  12. def initState(p: Parameters): MultivariateGaussianSvd

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  15. final def notify(): Unit

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  16. final def notifyAll(): Unit

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  17. def sampleState(states: List[List[(Double, DenseVector[Double])]], weights: List[Double]): Rand[List[(Double, DenseVector[Double])]]

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    Using the weights at time T (the end of all observations) sample a path from the collection of paths

    Using the weights at time T (the end of all observations) sample a path from the collection of paths

    states

    a collection of paths with ancestory, the outer list is of length T, inner length N

    weights

    particle weights at time T

    returns

    a single path

  18. def step(mod: Model, p: Parameters): (State, (Data, DenseVector[Double])) ⇒ State

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  23. final def wait(arg0: Long): Unit

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