ParticleMetropolisHastingsSerial

Instance Constructors

new ParticleMetropolisHastingsSerial(logLikelihood: (Parameters) ⇒ Id[LogLikelihood], transitionProb: (Parameters, Parameters) ⇒ LogLikelihood, proposal: (Parameters) ⇒ Rand[Parameters], initialParams: Parameters, prior: (Parameters) ⇒ LogLikelihood)

logLikelihood
a function from parameters to LogLikelihood
proposal
a generic proposal distribution for the metropolis algorithm (eg. Gaussian)
initialParams
the starting parameters for the metropolis algorithm

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
def finalize(): Unit

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

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

Definition Classes
AnyRef → Any
val initialParams: Parameters

the starting parameters for the metropolis algorithm
the starting parameters for the metropolis algorithm

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

Definition Classes
Any
val logLikelihood: (Parameters) ⇒ Id[LogLikelihood]

a function from parameters to LogLikelihood
a function from parameters to LogLikelihood

Definition Classes
ParticleMetropolisHastingsSerial → 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
ParticleMetropolisHastingsSerial → MetropolisHastings
def mhStep(p: ParamsState): Id[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
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
ParticleMetropolisHastingsSerial → MetropolisHastings
val prior: (Parameters) ⇒ LogLikelihood

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

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

a generic proposal distribution for the metropolis algorithm (eg.
a generic proposal distribution for the metropolis algorithm (eg. Gaussian)

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

Definition Classes
AnyRef
val transitionProb: (Parameters, Parameters) ⇒ LogLikelihood
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 ParticleMetropolisHastingsSerial(logLikelihood: (Parameters) ⇒ Id[LogLikelihood], transitionProb: (Parameters, Parameters) ⇒ LogLikelihood, proposal: (Parameters) ⇒ Rand[Parameters], initialParams: 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

def finalize(): Unit

def g: Monad[Id]

final def getClass(): Class[_]

val initialParams: Parameters

final def isInstanceOf[T0]: Boolean

val logLikelihood: (Parameters) ⇒ Id[LogLikelihood]

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

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

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

val transitionProb: (Parameters, Parameters) ⇒ LogLikelihood

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[Id]

Inherited from AnyRef

Inherited from Any

Ungrouped