ParticleMetropolisStateAsync

Instance Constructors

new ParticleMetropolisStateAsync(pf: (Parameters) ⇒ Future[(LogLikelihood, Vector[StateSpace])], initialParams: Parameters, proposal: (Parameters) ⇒ Rand[Parameters], prior: (Parameters) ⇒ LogLikelihood)(implicit ec: ExecutionContext)

Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def asInstanceOf[T0]: T0

Definition Classes
Any
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
implicit val ec: ExecutionContext
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
def g: Monad[Future]

Definition Classes
ParticleMetropolisStateAsync → MetropolisHastings
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
val initialParams: Parameters

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

Definition Classes
ParticleMetropolisStateAsync → MetropolisHastings
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
def iters: Source[MetropState, NotUsed]
def logLikelihood: (Parameters) ⇒ Future[LogLikelihood]

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

Definition Classes
ParticleMetropolisStateAsync → MetropolisHastings
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.
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. Normal) this cancels in the acceptance ratio
from
the previous parameter value
to
the proposed parameter value

Definition Classes
ParticleMetropolisStateAsync → MetropolisHastings
def mhStep(p: ParamsState): Future[ParamsState]

A single step of the metropolis hastings algorithm to be used with breeze implementation of Markov Chain.
A single step of the metropolis hastings algorithm to be used with breeze implementation of Markov Chain. This is an alteration to the implementation in breeze, here ParamsState holds on to the previous calculated pseudo marginal log-likelihood value so we don't need to run the previous particle filter again each iteration

Definition Classes
MetropolisHastings
def mhStepState: (MetropState) ⇒ Future[MetropState]
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
def params: Source[ParamsState, NotUsed]

Definition Classes
ParticleMetropolisStateAsync → MetropolisHastings
val pf: (Parameters) ⇒ Future[(LogLikelihood, Vector[StateSpace])]
val prior: (Parameters) ⇒ LogLikelihood

Prior distribution for the parameters, with default implementation
Prior distribution for the parameters, with default implementation

Definition Classes
ParticleMetropolisStateAsync → MetropolisHastings
val proposal: (Parameters) ⇒ Rand[Parameters]

Proposal density, to propose new parameters for a model
Proposal density, to propose new parameters for a model

Definition Classes
ParticleMetropolisStateAsync → MetropolisHastings
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )

Related Doc: package model

Instance Constructors

new ParticleMetropolisStateAsync(pf: (Parameters) ⇒ Future[(LogLikelihood, Vector[StateSpace])], initialParams: Parameters, proposal: (Parameters) ⇒ Rand[Parameters], prior: (Parameters) ⇒ LogLikelihood)(implicit ec: ExecutionContext)

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

implicit val ec: ExecutionContext

final def eq(arg0: AnyRef): Boolean

def finalize(): Unit

def g: Monad[Future]

final def getClass(): Class[_]

val initialParams: Parameters

final def isInstanceOf[T0]: Boolean

def iters: Source[MetropState, NotUsed]

def logLikelihood: (Parameters) ⇒ Future[LogLikelihood]

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

def mhStep(p: ParamsState): Future[ParamsState]

def mhStepState: (MetropState) ⇒ Future[MetropState]

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def params: Source[ParamsState, NotUsed]

val pf: (Parameters) ⇒ Future[(LogLikelihood, Vector[StateSpace])]

val prior: (Parameters) ⇒ LogLikelihood

val proposal: (Parameters) ⇒ Rand[Parameters]

final def synchronized[T0](arg0: ⇒ T0): T0

final def wait(): Unit

final def wait(arg0: Long, arg1: Int): Unit

final def wait(arg0: Long): Unit

Inherited from Serializable

Inherited from Serializable

Inherited from Product

Inherited from Equals

Inherited from MetropolisHastings[Future]

Inherited from AnyRef

Inherited from Any

Ungrouped