Abc

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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 equals(arg0: Any): Boolean

   

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8. def finalize(): Unit

   

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

   

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10. def hashCode(): Int

    

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11. final def isInstanceOf[T0]: Boolean

    

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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 run[P, D](n: Int, rprior: ⇒ P, dist: (P) ⇒ D): GenSeq[(P, D)]

    

    Function for running ABC simulations in parallel on all cores.

    Function for running ABC simulations in parallel on all cores. This function simply carries out the runs. No post-processing (including rejection) is carried out.

    n

    The number of ABC simulations to be carried out

    rprior

    Function returing a single simulated parameter value from a prior

    dist

    Function that takes a single parameter value and runs a forward simulation from the model, then computes distance of output from a target data set

    returns

    The collection of generated parameters together with their simulated distances from a target data set

16. def smc[P](N: Int, rprior: ⇒ P, dprior: (P) ⇒ LogLik, rdist: (P) ⇒ Double, rperturb: (P) ⇒ P, dperturb: (P, P) ⇒ LogLik, factor: Int = 10, steps: Int = 15, verb: Boolean = false): GenSeq[P]

    

    Function for running an ABC-SMC algorithm in parallel on all available cores.

    Function for running an ABC-SMC algorithm in parallel on all available cores.

    N

    Number of samples to be propagated forward at each sweep

    rprior

    Function for simulating from a prior on the parameters

    dprior

    Function returning the log-density of a parameter under the prior

    rdist

    Function which takes a parameter, runs a forward simultation, and then calculates a distance (as a scalar Double) from a target data set

    rperturb

    Function to perturb a parameter vector (perturbation kernel, in ABC-SMC speak)

    dperturb

    Function for evaluating the log-density of a perturbed parameter relative to an original parameter. In the case of a non-symmetric perturbation kernel, it is the new, perturbed value that is the first element of the tuple.

    factor

    The acceptance rate at each sweep is 1/factor

    steps

    The number of sweeps of the ABC-SMC algorithm to perform

    verb

    Print progress to the console?

    returns

    An equally weighted sample from the ABC-SMC parameter posterior, approximately of size N

17. def summary[P, D](output: GenSeq[(P, D)])(implicit arg0: CsvRow[P]): Unit

    

    Generate some basic diagnostics associated with an ABC run.

    Generate some basic diagnostics associated with an ABC run. Called purely for the side effect of generating output on the console.

    output

    Collection of runs (and distances) such as generated from run

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

    

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19. def toString(): String

    

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20. final def wait(): Unit

    

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

    

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

    

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