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1.  final def !=(arg0: Any): Boolean

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6.  def dlm(mod: Model, observations: Vector[Data], proposal: (Parameters) ⇒ ContinuousDistr[Parameters], prior: (Parameters) ⇒ Double, initP: Parameters): Process[State[Parameters]]

   Run Metropolis-Hastings algorithm for a DLM, using the kalman filter to calculate the likelihood

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13.  def mhStep[A](proposal: (A) ⇒ ContinuousDistr[A], prior: (A) ⇒ Double, likelihood: (A) ⇒ Double)(state: State[A]): Rand[State[A]]
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16.  final def notifyAll(): Unit

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17.  def pmmh(mod: Model, observations: Vector[Data], proposal: (Parameters) ⇒ ContinuousDistr[Parameters], prior: (Parameters) ⇒ Double, initP: Parameters, n: Int): Process[State[Parameters]]

    Particle Marginal Metropolis Hastings for a ContinuousTime Model Where the log-likelihood is an estimate calculated using the bootstrap particle filter

    Particle Marginal Metropolis Hastings for a ContinuousTime Model Where the log-likelihood is an estimate calculated using the bootstrap particle filter

    mod

    a DGLM model

    observations

    an array of observations

    initP

    the intial parameters to start the Markov Chain

    n

    the number of particles in the PF

    returns

    a Markov Chain Process which can be drawn from

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22.  final def wait(arg0: Long): Unit

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