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core.dlm.model

ParticleGibbsAncestor

object ParticleGibbsAncestor extends App

Particle Gibbs with Ancestor Sampling Requires a Tractable state evolution kernel

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App, DelayedInit, AnyRef, Any
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  1. ParticleGibbsAncestor
  2. App
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Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. def ancestorResampling(mod: Model, time: Double, states: Vector[List[(Double, DenseVector[Double])]], weights: Vector[Double], condState: DenseVector[Double], p: Parameters): (List[List[(Double, DenseVector[Double])]], List[(Double, DenseVector[Double])])

    Perform Ancestor Resampling on Particle Paths

    Perform Ancestor Resampling on Particle Paths

    mod

    a DGLM model

    time

    the time of the observation

    states

    a vector containing the latent states up to the current time

    weights

    a vector of weights calculated at the previous time step

    condState

    the previously conditioned upon path

    p

    the parameters of the model, including W

    returns

    a tuple where the first element is a list describing the paths from 0:time-1, and second element describes the particles at the current time

  5. def args: Array[String]
    Attributes
    protected
    Definition Classes
    App
    Annotations
    @deprecatedOverriding( "args should not be overridden" , "2.11.0" )
  6. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  7. def calcWeight(mod: Model, time: Double, xt: DenseVector[Double], xt1: DenseVector[Double], conditionedState: DenseVector[Double], y: DenseVector[Option[Double]], p: Parameters): Double

    The weights are proportional to the conditional likelihood of the observations given the state multiplied by the transition probability of the resampled particles at time t-1 to the conditioned state at time t TODO: This could be wrong, I don't think transition probability is calculated properly

    The weights are proportional to the conditional likelihood of the observations given the state multiplied by the transition probability of the resampled particles at time t-1 to the conditioned state at time t TODO: This could be wrong, I don't think transition probability is calculated properly

    mod

    the DGLM model

    time

    the current time, t

    xt

    the value of the state at time t

    xt1

    the value of the state at time t-1

    conditionedState

    the value of the conditioned state at time t

    y

    the observation at time t

    p

    the parameters of the model

    returns

    the conditional likeihood of the observation given the state and the transition probability to the next conditioned upon state

  8. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  9. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  10. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  11. val executionStart: Long
    Definition Classes
    App
    Annotations
    @deprecatedOverriding( ... , "2.11.0" )
  12. def filter(n: Int, p: Parameters, mod: Model, obs: List[Data])(state: List[(Double, DenseVector[Double])]): Rand[(Double, List[(Double, DenseVector[Double])])]

    Run Particle Gibbs with Ancestor Sampling

    Run Particle Gibbs with Ancestor Sampling

    n

    the number of particles to have in the sampler

    p

    the model parameters

    mod

    the DGLM model specification

    obs

    a list of measurements

    state

    the state which is to be conditioned upon

    returns

    a distribution over a tuple containing the log-likelihood and sampled state from the PGAS kernel

  13. def filterAll(n: Int, mod: Model, p: Parameters, obs: List[Data])(state: List[(Double, DenseVector[Double])]): State

    Run Particle Gibbs with Ancestor Sampling

    Run Particle Gibbs with Ancestor Sampling

    n

    the number of particles to have in the sampler

    mod

    the DGLM model specification

    p

    the model parameters

    obs

    a list of measurements

    state

    the state which is to be conditioned upon

    returns

    the state of the Particle Filter, including all Paths

  14. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  15. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  16. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  17. def importanceWeight(sampledStates: Vector[DenseVector[Double]], weights: Vector[Double], conditionedState: DenseVector[Double], time: Double, mod: Model, p: Parameters): Vector[Double]

    Calculate the importance weights for ancestor resampling the Nth path

  18. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  19. def logSumExp(w: Vector[Double]): Vector[Double]

    Recalculate the weights such that the smallest weight is 1

  20. def main(args: Array[String]): Unit
    Definition Classes
    App
    Annotations
    @deprecatedOverriding( "main should not be overridden" , "2.11.0" )
  21. def missingState(x: DenseVector[Double], y: DenseVector[Option[Double]]): DenseVector[Double]
  22. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  23. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  24. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  25. def sample[A](n: Int, xs: Vector[A], ws: Vector[Double]): Vector[A]

    Sample n items with replacement from xs with probability ws

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

    A single step in the Particle Gibbs with Ancestor Sampling algorithm

    A single step in the Particle Gibbs with Ancestor Sampling algorithm

    mod

    the model specification

    p

    the parameters of the model

    returns

    the state containing the particles, weights and running log-likelihood

  27. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  28. def toString(): String
    Definition Classes
    AnyRef → Any
  29. def transitionProbability(sampledState: DenseVector[Double], time: Double, mod: Model, p: Parameters)(conditionedState: DenseVector[Double]): Double

    Calculate the transition probability from a given particle at time t-1 to the conditioned upon particle at time t

    Calculate the transition probability from a given particle at time t-1 to the conditioned upon particle at time t

    sampledState

    a particle value at time t-1

    time

    the time t

    mod

    the DGLM model specification

    p

    the parameters of the model

    conditionedState

    the value of the conditioned state at time t

    returns

    the value of the log of the transition probability as a Double

  30. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  31. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  32. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )

Deprecated Value Members

  1. def delayedInit(body: ⇒ Unit): Unit
    Definition Classes
    App → DelayedInit
    Annotations
    @deprecated
    Deprecated

    (Since version 2.11.0) the delayedInit mechanism will disappear

Inherited from App

Inherited from DelayedInit

Inherited from AnyRef

Inherited from Any

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