Resampling

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 clone(): AnyRef

   

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6. def cumSum[F[_], A](l: F[A])(implicit f: Collection[F], N: Numeric[A]): F[A]

   

   Generic cumulative sum

7. def empDist[F[_]](w: F[Double])(implicit f: Collection[F]): F[Double]

   

   Calculate the empirical cumulative distribution function for a collection of weights

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

   

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9. def equals(arg0: Any): Boolean

   

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10. def expNormalise[F[_]](prob: F[LogLikelihood])(implicit f: Collection[F]): F[Double]

    

    Given a vector of log-likelihoods, normalise them and exp them without overflow

11. def finalize(): Unit

    

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12. def findAllInTreeMap[A](ks: Vector[Double], ecdf: TreeMap[Double, A]): Vector[A]

    

    Given a list of ordered doubles, k, find the element at the corresponding position in the empirical cumulative distribution function represented by a treeMap

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 multinomialResampling[A](particles: Vector[A], weights: Vector[LogLikelihood]): Vector[A]

    

    Multinomial Resampling, sample from a categorical distribution with probabilities equal to the particle weights

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

    

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18. def normalise[F[_]](prob: F[Double])(implicit f: Collection[F]): F[Double]

    

    Given a vector of doubles, returns a normalised vector with probabilities summing to one

    Given a vector of doubles, returns a normalised vector with probabilities summing to one

    prob

    a vector of unnormalised probabilities

    returns

    a vector of normalised probabilities

19. final def notify(): Unit

    

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20. final def notifyAll(): Unit

    

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21. def residualResampling[A](particles: Vector[A], weights: Vector[Double]): Vector[A]

    

    Residual Resampling Select particles in proportion to their weights, ie particle (xi, wi) appears ki = n * wi times Resample m = n - total allocated particles particles according to w = n * wi - ki, using other resampling technique

22. def sampleMany[A](n: Int, s: Vector[A]): IndexedSeq[A]

    

    Sample unifomly without replacement

23. def sampleOne[A](s: Seq[A]): A

    

    Sample one thing, uniformly, from a collection F

24. def stratifiedResampling[A](s: Vector[A], w: Vector[Double]): Vector[A]

    

    Stratified resampling implemented using a TreeMap Sample n ORDERED uniform random numbers (one for each particle) using a linear transformation of a U(0,1) RV

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

    

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26. def systematicResampling[A](particles: Vector[A], weights: Vector[LogLikelihood]): Vector[A]

    

    An efficient implementation of systematic resampling

27. def toString(): String

    

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28. def treeEcdf[A](items: Vector[A], prob: Vector[Double]): TreeMap[Double, A]

    

    Create an empirical cumulative distribution function from a set of particles and associated weights, and represent it as a treeMap

29. final def wait(): Unit

    

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

    

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

    

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