MetropolisHastings

abstract val initialParams: Parameters

The initial parameters, representing the place the Metropolis hastings algorithm starts

abstract def logLikelihood: (Parameters) ⇒ LogLikelihood

The likelihood function of the model, typically a pseudo-marginal likelihood estimated using the bootstrap particle filter for the PMMH algorithm

abstract def logTransition(from: Parameters, to: Parameters): LogLikelihood

Definition of the log-transition, used when calculating the acceptance ratio This is the probability of moving between parameters according to the proposal distribution Note: When using a symmetric proposal distribution (eg.

abstract def prior: (Parameters) ⇒ LogLikelihood

Prior distribution for the parameters, with default implementation

abstract def proposal: (Parameters) ⇒ Rand[Parameters]

Proposal density, to propose new parameters for a model

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 markovParams: Process[ParamsState]

Use the Breeze Markov Chain to generate a process of ParamsState Calling .sample(n) on this will create a single site metropolis hastings, proposing parameters only from the initial supplied parameter values

def mhStep: (ParamsState) ⇒ Rand[ParamsState]

A single step of the metropolis hastings algorithm to be used with breeze implementation of Markov Chain.

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def params: Source[ParamsState, NotUsed]

Use the same step for iterations in a stream

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