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(x: Seq[Double]): Seq[Double]

   

7. def diff(x: Iterable[Time]): Iterable[TimeIncrement]

   

   Return a vector of lag 1 time differences

   Return a vector of lag 1 time differences

   x

   a list of times

   returns

   a list of differenced times

8. def effectiveSampleSize(weights: Seq[LogLikelihood]): Int

   

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

   

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10. def equals(arg0: Any): Boolean

    

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11. def filter(t0: Time, n: Int): Reader[Model, Pipe[Task, Data, PfState]]

    

    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 finalize(): Unit

    

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13. def getAllCredibleIntervals(s: Seq[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

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

    

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15. def getCredibleInterval(s: Seq[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(mod: Model): (PfState) ⇒ PfOut

    

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

18. def getOrderStatistic(samples: Seq[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

19. def hashCode(): Int

    

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20. def hist(x: Seq[Int]): Unit

    

    Produces a histogram output of a vector of Data

21. def indentity[A](samples: Seq[A], weights: Seq[LogLikelihood]): Seq[A]

    

22. def invecdf[A](ecdf: Seq[(A, LogLikelihood)], p: Double): A

    

    Return the value x such that, F(p) = x, where F is the empirical cumulative distribution function over the particles

23. final def isInstanceOf[T0]: Boolean

    

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24. def likelihood(resamplingScheme: Resample[State], data: Seq[Data], n: Int): 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

    resamplingScheme

    the resampling scheme to use in the particle filter

    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

25. def meanState(x: Seq[State]): State

    

    Calculate the mean of a state

26. def multinomialResampling[A](particles: Seq[A], weights: Seq[LogLikelihood]): Seq[A]

    

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

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

    

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28. def normalise(prob: Seq[Double]): Seq[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

29. final def notify(): Unit

    

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30. final def notifyAll(): Unit

    

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31. def oneStepForecast(x0: List[State], t0: Time, t: Time, mod: Model): Rand[(ForecastOut, Seq[State])]

    

32. def residualResampling[A](particles: Seq[A], weights: Seq[LogLikelihood]): Seq[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

33. def sample(n: Int, prob: DenseVector[Double]): Seq[Int]

    

    Sample integers from 1 to n with replacement according to their associated probabilities

    Sample integers from 1 to n with replacement according to their associated probabilities

    n

    a number matching the number of probabilities

    prob

    a vector of probabilities corresponding to the probability of sampling that integer

    returns

    a vector containing the samples

34. def stratifiedResampling[A](particles: Seq[A], weights: Seq[LogLikelihood]): Seq[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[A](particles: Seq[A], weights: Seq[LogLikelihood]): Seq[A]

    

    Systematic Resampling Sample n ORDERED numbers (one for each particle), reusing the same U(0,1) variable

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: Seq[State], w: Seq[Double]): State