ParticleFilter

implicit abstract val f: Collection[F]

abstract val mod: Model

abstract def resample: Resample[F, State]

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

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

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

def advanceState(states: F[State], dt: TimeIncrement, t: Time): F[(State, Gamma)]

final def asInstanceOf[T0]: T0

def calculateWeights: (Gamma, Observation) ⇒ LogLikelihood

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

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

Run a filter over a stream of data

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

Filter the data, but get a vector containing the mean gamma, gamma 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[F]

final def isInstanceOf[T0]: Boolean

def llFilter(t0: Time, n: Int)(data: List[Data]): LogLikelihood

Filter a collection of data and return an estimate of the loglikelihood

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def stepFilter(s: PfState[F], y: Data): PfState[F]

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