SimulateData

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

   

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9. def forecast(unparamModel: (Parameters) ⇒ Model, t: Time)(s: Rand[(Parameters, State)]): Flow[Time, Rand[(Parameters, ObservationWithState)], NotUsed]

   

   Compute an empirical forecast, starting from a filtering distribution estimate

   Compute an empirical forecast, starting from a filtering distribution estimate

   unparamModel

   a function from Parameters to Model

   t

   the time to start the forecast

   s

   the joint posterior of the parameters and state at time t, p(x, theta | y)

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

    

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11. def getState(state: State, model: Model, position: Int)(t: Time): Eta

    

    Get the transformed state of the nth model

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17. def simSdeStream(x0: State, t0: Time, totalIncrement: TimeIncrement, precision: Int, stepFunction: (TimeIncrement) ⇒ (State) ⇒ Rand[State]): Stream[StateSpace]

    

    Simulate a diffusion process as a stream

    Simulate a diffusion process as a stream

    x0

    the starting value of the stream

    t0

    the starting time of the stream

    totalIncrement

    the ending time of the stream

    precision

    the step size of the stream 10e(-precision)

    returns

    a lazily evaluated stream of Sde

18. def simStep(model: Model)(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

    model

    the model to simulate a step from

    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

19. def summariseForecast(mod: (Parameters) ⇒ Model, interval: Double): (Vector[(Parameters, ObservationWithState)]) ⇒ ForecastOut

    

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