Dglm

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. final def asInstanceOf[T0]: T0

   

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5. def beta(mod: Dlm.Model): Model

   

   Construct a DGLM with Beta distributed observations, with variance < mean (1 - mean)

6. def beta(mean: Double, variance: Double): ContinuousDistr[Double]

   

   A beta distribution parameterised by the mean and variance

   A beta distribution parameterised by the mean and variance

   mean

   the mean of the resulting beta distribution

   variance

   the variance of the beta distribution

   returns

   a beta distribution

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 finalize(): Unit

    

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11. def forecastParticles(mod: Model, xt: Vector[DenseVector[Double]], time: Double, p: Parameters): Iterator[(Double, (DenseVector[Double], DenseMatrix[Double]))]

    

    Forecast a DLM from a particle cloud representing the latent state at the end of the observations

    Forecast a DLM from a particle cloud representing the latent state at the end of the observations

    mod

    the model

    xt

    the particle cloud representing the latent state

    time

    the initial time to start the forecast from

    p

    the parameters of the model

    returns

    the time, mean observation and variance of the observation

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

    

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13. def hashCode(): Int

    

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14. final def isInstanceOf[T0]: Boolean

    

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15. def logisticFunction(upper: Double)(number: Double): Double

    

    Logistic function to transform the number onto a range between 0 and upper

    Logistic function to transform the number onto a range between 0 and upper

    upper

    the upper limit of the logistic function

    number

    the number to be transformed

    returns

    a number between 0 and upper

16. def meanCovSamples(samples: Seq[DenseVector[Double]]): (DenseVector[Double], DenseMatrix[Double])

    

    Calculate the mean and covariance of a sequence of DenseVectors

17. def meanVarObservation(mod: Model, xt: Vector[DenseVector[Double]], v: DenseMatrix[Double]): Rand[(DenseVector[Double], DenseMatrix[Double])]

    

    Calculate the mean and variance of an observation at tim t given a particle cloud representing the latent-state at time t and the model specification

    Calculate the mean and variance of an observation at tim t given a particle cloud representing the latent-state at time t and the model specification

    mod

    a DGLM specification

    xt

    the particle cloud representing the latent state at time t

    v

    the observation noise variance

    returns

    mean and variance of observation

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

    

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19. final def notify(): Unit

    

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20. final def notifyAll(): Unit

    

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21. def poisson(mod: Dlm.Model): Model

    

    Construct a DGLM with Poisson distributed observations

22. def simStep(mod: Model, p: Parameters): (Double, DenseVector[Double]) ⇒ Rand[(Data, DenseVector[Double])]

    

    Simulate a single step of a DGLM model

23. def simulate(mod: Model, p: Parameters): Process[(Data, DenseVector[Double])]

    

    Simulate from a dlm model at regular time intervals

24. def studentT(df: Int, mod: Dlm.Model): Model

    

    Define a DGLM with Student's t observation errors

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

    

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26. def toString(): String

    

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27. final def wait(): Unit

    

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

    

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

    

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