SimulateData

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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 clone(): AnyRef

   

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

   

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7. def finalize(): Unit

   

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8. def forecast[F[_], G[_]](s: PfState[F])(implicit f: Collection[F]): Flow[Double, F[ObservationWithState], NotUsed]

   

   Compute an empirical forecast, starting from a filtering distribution estimate

   Compute an empirical forecast, starting from a filtering distribution estimate

   s

   a PfState object, the output of a particle filter

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

   

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10. final def isInstanceOf[T0]: Boolean

    

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11. val model: Model

    

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

    

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13. final def notify(): Unit

    

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14. final def notifyAll(): Unit

    

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15. def observations: Source[Data, NotUsed]

    

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    SimulateData → DataService

16. def simLGCP(start: Time, end: Time, precision: Int): Vector[ObservationWithState]

    

    Simulate the log-Gaussian Cox-Process using thinning

    Simulate the log-Gaussian Cox-Process using thinning

    start

    the starting time of the process

    precision

    an integer specifying the timestep between simulating the latent state, 10e-precision

    returns

    a vector of Data specifying when events happened

17. def simMarkov(dt: TimeIncrement): Process[ObservationWithState]

    

    Simulate from a POMP model (not including the Log-Gaussian Cox-Process) on a regular grid from t = 0 using the MarkovChain from the breeze package

    Simulate from a POMP model (not including the Log-Gaussian Cox-Process) on a regular grid from t = 0 using the MarkovChain from the breeze package

    dt

    the time increment between sucessive realisations of the POMP model

    returns

    a Process, representing a distribution which depends on previous draws

18. def simPompModel(t0: Time): Flow[Double, ObservationWithState, NotUsed]

    

    Simulate from a POMP model on an irregular grid, given an initial time and a stream of times at which simulate from the model

    Simulate from a POMP model on an irregular grid, given an initial time and a stream of times at which simulate from the model

    t0

    the start time of the process

    returns

    an Akka Flow transforming a Stream from Time to ObservationWithState

19. def simRegular(dt: TimeIncrement): Source[ObservationWithState, NotUsed]

    

    Simulate from any model on a regular grid from t = 0 and return an Akka stream of realisations

    Simulate from any model on a regular grid from t = 0 and return an Akka stream of realisations

    dt

    the time increment between successive realisations of the POMP model

    returns

    an Akka Stream containing a realisation of the process

20. def simStep(deltat: TimeIncrement): (ObservationWithState) ⇒ Rand[ObservationWithState]

    

    Simulate a single step from a model, return a distribution over the possible values of the next step

    Simulate a single step from a model, return a distribution over the possible values of the next step

    deltat

    the time difference between the previous and next realisation of the process

    returns

    a function from the previous datapoint to a Rand (Monadic distribution) representing the distribution of the next datapoint

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

    

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22. final def wait(): Unit

    

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

    

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

    

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