ParticleMetropolisState

Instance Constructors

new ParticleMetropolisState(filter: (Parameters) ⇒ (LogLikelihood, Vector[State]), initialParams: Parameters, proposal: (Parameters) ⇒ Rand[Parameters], prior: (Parameters) ⇒ LogLikelihood)

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( ... )
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
val filter: (Parameters) ⇒ (LogLikelihood, Vector[State])
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
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
ParticleMetropolisState → MetropolisHastings
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
def iters: Process[MetropState]

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

Definition Classes
ParticleMetropolisState → 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
ParticleMetropolisState → MetropolisHastings
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
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

Definition Classes
MetropolisHastings
def mhStep: (ParamsState) ⇒ Rand[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 a slight 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) ⇒ Rand[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]

Use the same step for iterations in a stream
Use the same step for iterations in a stream

Definition Classes
MetropolisHastings
val prior: (Parameters) ⇒ LogLikelihood

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

Definition Classes
ParticleMetropolisState → 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
ParticleMetropolisState → 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

case class ParticleMetropolisState(filter: (Parameters) ⇒ (LogLikelihood, Vector[State]), initialParams: Parameters, proposal: (Parameters) ⇒ Rand[Parameters], prior: (Parameters) ⇒ LogLikelihood) extends MetropolisHastings with Product with Serializable

Instance Constructors

new ParticleMetropolisState(filter: (Parameters) ⇒ (LogLikelihood, Vector[State]), initialParams: Parameters, proposal: (Parameters) ⇒ Rand[Parameters], prior: (Parameters) ⇒ LogLikelihood)

Value Members

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

val filter: (Parameters) ⇒ (LogLikelihood, Vector[State])

def finalize(): Unit

final def getClass(): Class[_]

val initialParams: Parameters

final def isInstanceOf[T0]: Boolean

def iters: Process[MetropState]

def logLikelihood: (Parameters) ⇒ LogLikelihood

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

def markovParams: Process[ParamsState]

def mhStep: (ParamsState) ⇒ Rand[ParamsState]

def mhStepState: (MetropState) ⇒ Rand[MetropState]

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def params: Source[ParamsState, NotUsed]

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

Inherited from AnyRef

Inherited from Any

Ungrouped