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 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 iters: Process[MetropState]

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

def itersAkka: Source[MetropState, Any]

Generates an akka stream of MetropState, containing the current parameters, count of accepted moves and the current pseudo marginal log-likelihood Unfortunately for an unknown reason this isn't working

def itersSeq(n: Int): Seq[MetropState]

Returns iterations from the MCMC algorithm in a vector using sampleStep, sampleStep

def itersStream: Source[MetropState, Any]

Use the same step for iterations in a stream

def mhStep: (MetropState) ⇒ MetropState

Generic metropolis-hastings step, which can be used with the usual acceptance ratio or simplified to the metropolis ratio by specifying the log-transition of the parameters to be zero

def mhStepRand: (MetropState) ⇒ Rand[MetropState]

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 paramsAkka: Source[Parameters, Any]

Return an akka stream of the parameters

def prior: ContinuousDistr[Parameters]

Prior distribution for the parameters, with default implementation

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