ParticleMetropolisHastings

Instance Constructors

new ParticleMetropolisHastings(logLikelihood: (Parameters) ⇒ 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] )
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
ParticleMetropolisHastings → MetropolisHastings
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
val logLikelihood: (Parameters) ⇒ LogLikelihood

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

Definition Classes
ParticleMetropolisHastings → 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
ParticleMetropolisHastings → 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
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
ParticleMetropolisHastings → 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
ParticleMetropolisHastings → 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 ParticleMetropolisHastings(logLikelihood: (Parameters) ⇒ 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

final def getClass(): Class[_]

val initialParams: Parameters

final def isInstanceOf[T0]: Boolean

val logLikelihood: (Parameters) ⇒ LogLikelihood

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

def markovParams: Process[ParamsState]

def mhStep: (ParamsState) ⇒ Rand[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

Inherited from AnyRef

Inherited from Any

Ungrouped