ParticleFilter

abstract def advanceState(x: Seq[State], dt: TimeIncrement, t: Time): Seq[(State, Eta)]

abstract def calculateWeights(x: Eta, y: Observation): LogLikelihood

abstract val mod: Model

A model

abstract def resample: Resample[State]

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

def accFilter(data: Seq[Data], t0: Time)(particles: Int): Seq[PfState]

Run a filter over a vector of data and return a vector of PfState Containing the raw particles and associated weights at each time step

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def filter(t0: Time)(particles: Int): Pipe[Task, Data, PfState]

Run a filter over a stream of data

def filterWithForecast(t0: Time)(particles: Int): Pipe[Task, Data, (PfState, ForecastOut)]

One step forecast filter, advances the particles ahead to the time of the next observation transforms them according to the model and calculates the expected observation and 99% credible intervals

def filterWithIntervals(data: Seq[Data], t0: Time)(particles: Int): Seq[PfOut]

Filter the data, but get a vector containing the mean eta, eta intervals, mean state, and credible intervals of the state

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

def initialiseState(particles: Int, t0: Time): PfState

final def isInstanceOf[T0]: Boolean

def llFilter(t0: Time, particles: Int)(data: Seq[Data]): LogLikelihood

Calculate the log-likelihood

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def stepFilter(s: PfState, y: Data): PfState

Step filter, perform one step of the particle fiilter

def stepWithForecast(s: PfState, y: Data): (PfState, ForecastOut)

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

def toString(): String

final def wait(): Unit

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

final def wait(arg0: Long): Unit