Model

abstract def dataLikelihood: (Gamma, Observation) ⇒ LogLikelihood

The data likelihood, given a fully transformed latent state, eta, and an observation the log-likelihood can be calculated for use in inference algorithms

abstract def f(s: State, t: Time): Gamma

The Linear, deterministic transformation function.

abstract def observation: (Gamma) ⇒ Rand[Observation]

The observation model, a function from eta to a distribution over the observations realisations can be produced from the observation model by calling draw

abstract def sde: Sde

An exact or approximate solution to a diffusion process, used to advance the latent state.

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

def link(x: Gamma): Eta

The linking-function, transforms the state space into the parameter space of the observation distribution using a possibly non-linear transformation

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

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