GibbsSampling

Type Members

case class State(p: Parameters, state: Array[(Time, DenseVector[Double])]) extends Product with Serializable

Value Members

final def !=(arg0: Any): Boolean

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final def ##(): Int

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final def ==(arg0: Any): Boolean

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def args: Array[String]

Attributes
protected
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App
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@deprecatedOverriding( "args should not be overridden" , "2.11.0" )
final def asInstanceOf[T0]: T0

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def clone(): AnyRef

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protected[java.lang]
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@throws( ... )
def dinvGammaStep(mod: Model, priorV: InverseGamma, priorW: InverseGamma, observations: Array[Data])(gibbsState: State): Rand[State]

A single step of a Gibbs Sampler
final def eq(arg0: AnyRef): Boolean

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def equals(arg0: Any): Boolean

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val executionStart: Long

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App
def finalize(): Unit

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protected[java.lang]
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@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

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def gibbsMetropSamples(proposal: (Parameters) ⇒ Rand[Parameters], mod: Model, priorV: InverseGamma, priorW: InverseGamma, initParams: Parameters, observations: Array[Data]): Process[State]
def gibbsMetropStep(proposal: (Parameters) ⇒ Rand[Parameters], mod: Model, priorV: InverseGamma, priorW: InverseGamma, observations: Array[Data])(gibbsState: State): Rand[State]
def gibbsSamples(mod: Model, priorV: InverseGamma, priorW: InverseGamma, initParams: Parameters, observations: Array[Data]): Process[State]

Return a Markov chain using Gibbs Sampling to determine the values of the system and observation noise covariance matrices, W and V
Return a Markov chain using Gibbs Sampling to determine the values of the system and observation noise covariance matrices, W and V
mod
the model containing the definition of the observation matrix F_t and system evolution matrix G_t
priorV
the prior distribution on the observation noise matrix, V
priorW
the prior distribution on the system noise matrix, W
initParams
the initial parameters of the Markov Chain
observations
an array of Data containing the observed time series
returns
a Process
def hashCode(): Int

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final def isInstanceOf[T0]: Boolean

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def main(args: Array[String]): Unit

Definition Classes
App
Annotations
@deprecatedOverriding( "main should not be overridden" , "2.11.0" )
def metropStep(mod: Model, observations: Array[Data], proposal: (Parameters) ⇒ Rand[Parameters]): (Parameters) ⇒ Rand[Parameters]
final def ne(arg0: AnyRef): Boolean

Definition Classes
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final def notify(): Unit

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final def notifyAll(): Unit

Definition Classes
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def observationSquaredDifference(mod: Model, state: Array[(Time, DenseVector[Double])], observations: Array[Data]): DenseVector[Double]

Calculate the sum of squared differences between the one step forecast and the actual observation for each time sum((y_t - f_t)^2)
Calculate the sum of squared differences between the one step forecast and the actual observation for each time sum((y_t - f_t)^2)
mod
a model specifying the observation and system evolution matrices
state
an array containing the state sampled from the backward sampling algorithm
observations
an array containing the actual observations of the data
returns
the sum of squared differences between the one step forecast and the actual observation for each time
def sampleObservationMatrix(prior: InverseGamma, mod: Model, state: Array[(Time, DenseVector[Double])], observations: Array[Data]): Rand[DenseMatrix[Double]]

Sample the (diagonal) observation noise covariance matrix from an Inverse Gamma distribution
Sample the (diagonal) observation noise covariance matrix from an Inverse Gamma distribution
prior
an Inverse Gamma prior distribution for each variance element of the observation matrix
mod
the DLM specification
state
a sample of the DLM state
observations
the observed values of the time series
returns
the posterior distribution over the diagonal observation matrix
def sampleState(mod: Model, observations: Array[Data], p: Parameters): Array[(Time, DenseVector[Double])]

Sample state
def sampleSystemMatrix(prior: InverseGamma, mod: Model, state: Array[(Time, DenseVector[Double])]): Rand[DenseMatrix[Double]]

Sample the (diagonal) system noise covariance matrix from an inverse gamma
def stateSquaredDifference(mod: Model, state: Array[(Time, DenseVector[Double])]): DenseVector[Double]
final def synchronized[T0](arg0: ⇒ T0): T0

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def toString(): String

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final def wait(): Unit

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

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Deprecated Value Members

def delayedInit(body: ⇒ Unit): Unit

Definition Classes
App → DelayedInit
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@deprecated
Deprecated
(Since version 2.11.0) The delayedInit mechanism will disappear.

Related Doc: package model

object GibbsSampling extends App

Type Members

case class State(p: Parameters, state: Array[(Time, DenseVector[Double])]) extends Product with Serializable

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

def args: Array[String]

final def asInstanceOf[T0]: T0

def clone(): AnyRef

def dinvGammaStep(mod: Model, priorV: InverseGamma, priorW: InverseGamma, observations: Array[Data])(gibbsState: State): Rand[State]

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

val executionStart: Long

def finalize(): Unit

final def getClass(): Class[_]

def gibbsMetropSamples(proposal: (Parameters) ⇒ Rand[Parameters], mod: Model, priorV: InverseGamma, priorW: InverseGamma, initParams: Parameters, observations: Array[Data]): Process[State]

def gibbsMetropStep(proposal: (Parameters) ⇒ Rand[Parameters], mod: Model, priorV: InverseGamma, priorW: InverseGamma, observations: Array[Data])(gibbsState: State): Rand[State]

def gibbsSamples(mod: Model, priorV: InverseGamma, priorW: InverseGamma, initParams: Parameters, observations: Array[Data]): Process[State]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

def main(args: Array[String]): Unit

def metropStep(mod: Model, observations: Array[Data], proposal: (Parameters) ⇒ Rand[Parameters]): (Parameters) ⇒ Rand[Parameters]

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def observationSquaredDifference(mod: Model, state: Array[(Time, DenseVector[Double])], observations: Array[Data]): DenseVector[Double]

def sampleObservationMatrix(prior: InverseGamma, mod: Model, state: Array[(Time, DenseVector[Double])], observations: Array[Data]): Rand[DenseMatrix[Double]]

def sampleState(mod: Model, observations: Array[Data], p: Parameters): Array[(Time, DenseVector[Double])]

def sampleSystemMatrix(prior: InverseGamma, mod: Model, state: Array[(Time, DenseVector[Double])]): Rand[DenseMatrix[Double]]

def stateSquaredDifference(mod: Model, state: Array[(Time, DenseVector[Double])]): DenseVector[Double]

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

Deprecated Value Members

def delayedInit(body: ⇒ Unit): Unit

Inherited from App

Inherited from DelayedInit

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