ParticleFilter

Value Members

1. final def !=(arg0: Any): Boolean

   

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2. final def ##(): Int

   

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3. final def ==(arg0: Any): Boolean

   

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4. def advanceState[F[_]](g: (Double) ⇒ DenseMatrix[Double], time: Double, state: F[DenseVector[Double]], p: Parameters)(implicit arg0: Traverse[F]): Rand[F[DenseVector[Double]]]

   

   Advance the system of particles to the next timepoint

   Advance the system of particles to the next timepoint

   g

   the system evolution matrix, G_t

   time

   the time of the next observation

   state

   a collection of particles representing time emprical posterior distribution of the state at time - 1

   p

   the parameters of the model, containing the system evolution matrix, W

   returns

   a distribution over a collection of particles at this time

5. final def asInstanceOf[T0]: T0

   

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6. def calcWeights[F[_]](mod: Model, time: Double, state: F[DenseVector[Double]], y: DenseVector[Option[Double]], p: Parameters)(implicit arg0: Functor[F]): F[Double]

   

   Calculate the weights of each particle using the conditional likelihood of the observations given the state

   Calculate the weights of each particle using the conditional likelihood of the observations given the state

   mod

   the DGLM model specification containing F_t and G_t, and the conditional likelihood of observations

   time

   the time of the observation

   state

   a collection of particles representing time emprical prior distribution of the state at this time

   y

   the observation observation given a value of the state, p(y_t | F_t x_t)

   returns

   a collection of weights corresponding to each particle

7. def clone(): AnyRef

   

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8. final def eq(arg0: AnyRef): Boolean

   

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9. def equals(arg0: Any): Boolean

   

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10. def filter(mod: Model, observations: Seq[Data], p: Parameters, n: Int): Seq[State]

    

    Run a Boostrap Particle Filter over a DGLM

    Run a Boostrap Particle Filter over a DGLM

    mod

    a Model class containing the specification of G and F

    observations

    a Seq of Data

    p

    the parameters used to run the particle filter

    n

    the number of particles to use in the filter, more results in higher accuracy

11. def filterStep(mod: Model, p: Parameters)(s: State, d: Data): State

    

12. def finalize(): Unit

    

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13. final def getClass(): Class[_]

    

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14. def hashCode(): Int

    

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15. final def isInstanceOf[T0]: Boolean

    

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16. def likelihood(mod: Model, observations: Vector[Data], n: Int)(p: Parameters): Double

    

    Run a Bootstrap Particle Filter over a DGLM to calculate the log-likelihood

17. final def ne(arg0: AnyRef): Boolean

    

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18. final def notify(): Unit

    

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19. final def notifyAll(): Unit

    

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20. def resample[A](particles: Vector[A], weights: Vector[Double]): Vector[A]

    

    Multinomial Resampling

21. final def synchronized[T0](arg0: ⇒ T0): T0

    

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22. def toString(): String

    

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23. final def wait(): Unit

    

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24. final def wait(arg0: Long, arg1: Int): Unit

    

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

    

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