GibbsSampling

Type Members

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

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
def args: Array[String]

Attributes
protected
Definition Classes
App
Annotations
@deprecatedOverriding( "args should not be overridden" , "2.11.0" )
def arlikelihood(state: Vector[(Double, DenseVector[Double])], p: Parameters, phi: DenseVector[Double]): Double

Calculate the marginal likelihood of phi given the values of the latent-state and other static parameters
Calculate the marginal likelihood of phi given the values of the latent-state and other static parameters
state
a sample of the latent state of an AR(1) DLM
p
the static parameters of a DLM
phi
autoregressive
final def asInstanceOf[T0]: T0

Definition Classes
Any
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
def dinvGammaStep(mod: Model, priorV: InverseGamma, priorW: InverseGamma, observations: Vector[Data]): (State) ⇒ Rand[State]

A single step of a Gibbs Sampler
A single step of a Gibbs Sampler
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
observations
an array of Data containing the observed time series
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
val executionStart: Long

Definition Classes
App
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
def gibbsMetropStep(proposal: (Parameters) ⇒ Rand[Parameters], mod: Model, priorV: InverseGamma, priorW: InverseGamma, observations: Vector[Data]): (State) ⇒ Rand[State]

Use metropolis hastings to determine the initial state distribution x0 ~ N(m0, C0)
Use metropolis hastings to determine the initial state distribution x0 ~ N(m0, C0)
proposal
a proposal distribution for the parameters of the initial state
mod
a DLM model specification
priorV
the prior distribution of the observation noise matrix
priorW
the prior distribution of the system noise matrix
observations
a vector of observations
def hashCode(): Int

Definition Classes
AnyRef → Any
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
def likelihood(theta: Vector[(Double, DenseVector[Double])], g: (Double) ⇒ DenseMatrix[Double])(p: Parameters): Double

Calculate the marginal likelihood for metropolis hastings
def main(args: Array[String]): Unit

Definition Classes
App
Annotations
@deprecatedOverriding( "main should not be overridden" , "2.11.0" )
def metropSamples(proposal: (Parameters) ⇒ Rand[Parameters], mod: Model, priorV: InverseGamma, priorW: InverseGamma, initParams: Parameters, observations: Vector[Data]): Process[State]
def metropStep(mod: Model, theta: Vector[(Double, DenseVector[Double])], proposal: (Parameters) ⇒ Rand[Parameters]): (Parameters) ⇒ Rand[Parameters]

A metropolis step for a DLM
A metropolis step for a DLM
mod
a DLM model
theta
the currently sampled state of the DLM
proposal
a symmetric proposal distribution for the parameters of a DLM
returns
a function from Parameters => Rand[Parameters] which performs a metropolis step to be used in a Markov Chain
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
def sample(mod: Model, priorV: InverseGamma, priorW: InverseGamma, initParams: Parameters, observations: Vector[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 sampleObservationMatrix(prior: InverseGamma, f: (Double) ⇒ DenseMatrix[Double], ys: Vector[Data], theta: Vector[(Double, DenseVector[Double])]): 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
ys
the observed values of the time series
theta
a sample of the DLM state
returns
the posterior distribution over the diagonal observation matrix
def samplePhi(prior: Beta, lambda: Double, tau: Double, s: State): (Double) ⇒ Rand[Double]

Sample the autoregressive parameter with a Beta Prior and proposal distribution
def sampleSystemMatrix(prior: InverseGamma, theta: Vector[(Double, DenseVector[Double])], g: (Double) ⇒ DenseMatrix[Double]): Rand[DenseMatrix[Double]]

Sample the diagonal system matrix for an irregularly observed DLM
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
AnyRef → Any
def updateModel(mod: Model, phi: Double*): Model

Update an autoregressive model with a new value of the autoregressive parameter
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( ... )

Deprecated Value Members

def delayedInit(body: ⇒ Unit): Unit

Definition Classes
App → DelayedInit
Annotations
@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: Vector[(Double, 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]

def arlikelihood(state: Vector[(Double, DenseVector[Double])], p: Parameters, phi: DenseVector[Double]): Double

final def asInstanceOf[T0]: T0

def clone(): AnyRef

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

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

def likelihood(theta: Vector[(Double, DenseVector[Double])], g: (Double) ⇒ DenseMatrix[Double])(p: Parameters): Double

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

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

def metropStep(mod: Model, theta: Vector[(Double, DenseVector[Double])], proposal: (Parameters) ⇒ Rand[Parameters]): (Parameters) ⇒ Rand[Parameters]

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

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

def sampleObservationMatrix(prior: InverseGamma, f: (Double) ⇒ DenseMatrix[Double], ys: Vector[Data], theta: Vector[(Double, DenseVector[Double])]): Rand[DenseMatrix[Double]]

def samplePhi(prior: Beta, lambda: Double, tau: Double, s: State): (Double) ⇒ Rand[Double]

def sampleSystemMatrix(prior: InverseGamma, theta: Vector[(Double, DenseVector[Double])], g: (Double) ⇒ DenseMatrix[Double]): Rand[DenseMatrix[Double]]

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

def toString(): String

def updateModel(mod: Model, phi: Double*): Model

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

Inherited from AnyRef

Inherited from Any

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