ParticleFilter

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 ecdf[F[_]](w: F[Double])(implicit f: Collection[F]): F[Double]

   

   Calculate the empirical cumulative distribution function for a collection of weights

8. def effectiveSampleSize[F[_]](weights: F[Double])(implicit f: Collection[F]): Int

   

   Calculate the effective sample size of a particle cloud, from the un-normalised weights by first normalising the weights, then calculating the reciprocal of the sum of the squared weights

   Calculate the effective sample size of a particle cloud, from the un-normalised weights by first normalising the weights, then calculating the reciprocal of the sum of the squared weights

   weights

   the unnormalised weights

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

   

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10. def equals(arg0: Any): Boolean

    

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11. def filter[F[_]](resample: Resample[F, State], t0: Time, n: Int)(implicit arg0: Collection[F]): Reader[Model, Pipe[Task, Data, PfState[F]]]

    

    Construct a particle filter to calculate the state of an fs2 Stream of Data

    Construct a particle filter to calculate the state of an fs2 Stream of Data

    t0

    the starting time of the observations

    n

    the number of particles to use in the filter

    returns

    a Reader monad representing the function Model => Pipe[Task, Data, PfState] When given a Model, this can be used to filter an fs2 stream of data eg: val mod: Model val pf = filter(0.0, 100) val data: Stream[Task, Data] = // data as an fs2 stream data. through(pf(mod))

12. def filterWithIntervals[F[_]](data: List[Data], resample: Resample[F, State], t0: Time, n: Int)(implicit arg0: Collection[F]): Reader[Model, List[PfOut]]

    

13. def finalize(): Unit

    

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

    

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15. def getCredibleInterval(s: List[State], n: Int, interval: Double): IndexedSeq[CredibleInterval]

    

    Get the credible intervals of the nth state vector

    Get the credible intervals of the nth state vector

    s

    a State

    n

    a reference to a node of state tree, counting from 0 on the left

    interval

    the probability interval size

    returns

    a tuple of doubles, (lower, upper)

16. def getCredibleIntervals(x0: Rand[State], interval: Double): IndexedSeq[CredibleInterval]

    

    Given a distribution over State, calculate credible intervals by repeatedly drawing from the distribution and ordering the samples

17. def getIntervals[F[_]](mod: Model)(implicit f: Collection[F]): (PfState[F]) ⇒ PfOut

    

    Transforms PfState into PfOut, including gamma, gamma intervals and state intervals

18. def getMeanForecast[F[_]](s: PfState[F], mod: Model, t: Time)(implicit f: Collection[F]): ForecastOut

    

    Given a state of the particle filter, advance the state and calculate the mean of the state, gamma and forecast observation

    Given a state of the particle filter, advance the state and calculate the mean of the state, gamma and forecast observation

    s

    the state of the particle filter

    mod

    the model used to predict the observations

    t

    the time of the prediction

    returns

    ForecastOut, a summary containing the mean of the state, gamma and observation

19. def getOrderStatistic(samples: List[Double], interval: Double): CredibleInterval

    

    Gets credible intervals for a vector of doubles

    Gets credible intervals for a vector of doubles

    samples

    a vector of samples from a distribution

    interval

    the upper interval of the required credible interval

    returns

    order statistics representing the credible interval of the samples vector

20. def getallCredibleIntervals(s: List[State], interval: Double): IndexedSeq[CredibleInterval]

    

    Use getCredibleInterval to get all credible intervals of a state

    Use getCredibleInterval to get all credible intervals of a state

    s

    a vector of states

    interval

    the interval for the probability interval between [0,1]

    returns

    a sequence of tuples, (lower, upper) corresponding to each state reading

21. def hashCode(): Int

    

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22. def hist(x: List[Int]): Unit

    

    Produces a histogram output of a vector of Data

23. def indentity[A](samples: List[A], weights: List[Double]): List[A]

    

24. final def isInstanceOf[T0]: Boolean

    

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25. def likelihood(data: List[Data], n: Int, resample: Resample[List, State]): Reader[Model, LogLikelihood]

    

    Construct a particle filter to determine the pseudo-marginal likelihood of a POMP model This function returns the Reader Monad (Reader[Model, LogLikelihood]) which means the function can be composed with a model: val mod: Reader[Parameters, Model] = // any model here val mll: Reader[Parameters, Likelihood] = likelihood(_, _, _) compose mod To get the function back from inside the Reader monad, simply use mll.run

    Construct a particle filter to determine the pseudo-marginal likelihood of a POMP model This function returns the Reader Monad (Reader[Model, LogLikelihood]) which means the function can be composed with a model: val mod: Reader[Parameters, Model] = // any model here val mll: Reader[Parameters, Likelihood] = likelihood(_, _, _) compose mod To get the function back from inside the Reader monad, simply use mll.run

    data

    a sequence of data to determine the likelihood of

    n

    the number of particles to use in the particle filter

    returns

    a function from model to logLikelihood

26. def meanState(x: List[State]): State

    

    Calculate the mean of a state

27. def multinomialResampling[F[_], A](particles: F[A], weights: F[LogLikelihood])(implicit f: Collection[F]): F[A]

    

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

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

    

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29. 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

30. final def notify(): Unit

    

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31. final def notifyAll(): Unit

    

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32. def parLikelihood(data: List[Data], n: Int)(implicit parallelism: Int = 4): Reader[Model, LogLikelihood]

    

33. def residualResampling[A](particles: List[A], weights: List[Double]): List[A]

    

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

34. def stratifiedResampling[F[_], A](s: F[A], w: F[Double])(implicit f: Collection[F]): F[A]

    

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

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

    

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36. def systematicResampling[F[_], A](s: F[A], w: F[Double])(implicit f: Collection[F]): F[A]

    

    Generic systematic Resampling

    Generic systematic Resampling

    Write SBT doctest scalacheck tests

37. def toString(): String

    

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38. final def wait(): Unit

    

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

    

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

    

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41. def weightedMean(x: List[State], w: List[Double]): State

    

    Calculate the weighted mean of a particle cloud