ParticleGibbsAncestor

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 ancestorResampling(mod: Model, time: Double, states: Vector[List[(Double, DenseVector[Double])]], weights: Vector[Double], condState: DenseVector[Double], p: Parameters): (List[List[(Double, DenseVector[Double])]], List[(Double, DenseVector[Double])])

Perform Ancestor Resampling on Particle Paths
Perform Ancestor Resampling on Particle Paths
mod
a DGLM model
time
the time of the observation
states
a vector containing the latent states up to the current time
weights
a vector of weights calculated at the previous time step
condState
the previously conditioned upon path
p
the parameters of the model, including W
returns
a tuple where the first element is a list describing the paths from 0:time-1, and second element describes the particles at the current time
def args: Array[String]

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

Definition Classes
Any
def calcWeight(mod: Model, time: Double, xt: DenseVector[Double], xt1: DenseVector[Double], conditionedState: DenseVector[Double], y: DenseVector[Option[Double]], p: Parameters): Double

The weights are proportional to the conditional likelihood of the observations given the state multiplied by the transition probability of the resampled particles at time t-1 to the conditioned state at time t TODO: This could be wrong, I don't think transition probability is calculated properly
The weights are proportional to the conditional likelihood of the observations given the state multiplied by the transition probability of the resampled particles at time t-1 to the conditioned state at time t TODO: This could be wrong, I don't think transition probability is calculated properly
mod
the DGLM model
time
the current time, t
xt
the value of the state at time t
xt1
the value of the state at time t-1
conditionedState
the value of the conditioned state at time t
y
the observation at time t
p
the parameters of the model
returns
the conditional likeihood of the observation given the state and the transition probability to the next conditioned upon state
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
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 filter(n: Int, p: Parameters, mod: Model, obs: List[Data])(state: List[(Double, DenseVector[Double])]): Rand[(Double, List[(Double, DenseVector[Double])])]

Run Particle Gibbs with Ancestor Sampling
Run Particle Gibbs with Ancestor Sampling
n
the number of particles to have in the sampler
p
the model parameters
mod
the DGLM model specification
obs
a list of measurements
state
the state which is to be conditioned upon
returns
a distribution over a tuple containing the log-likelihood and sampled state from the PGAS kernel
def filterAll(n: Int, mod: Model, p: Parameters, obs: List[Data])(state: List[(Double, DenseVector[Double])]): State

Run Particle Gibbs with Ancestor Sampling
Run Particle Gibbs with Ancestor Sampling
n
the number of particles to have in the sampler
mod
the DGLM model specification
p
the model parameters
obs
a list of measurements
state
the state which is to be conditioned upon
returns
the state of the Particle Filter, including all Paths
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 hashCode(): Int

Definition Classes
AnyRef → Any
def importanceWeight(sampledStates: Vector[DenseVector[Double]], weights: Vector[Double], conditionedState: DenseVector[Double], time: Double, mod: Model, p: Parameters): Vector[Double]

Calculate the importance weights for ancestor resampling the Nth path
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
def logSumExp(w: Vector[Double]): Vector[Double]

Recalculate the weights such that the smallest weight is 1
def main(args: Array[String]): Unit

Definition Classes
App
Annotations
@deprecatedOverriding( "main should not be overridden" , "2.11.0" )
def missingState(x: DenseVector[Double], y: DenseVector[Option[Double]]): DenseVector[Double]
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[A](n: Int, xs: Vector[A], ws: Vector[Double]): Vector[A]

Sample n items with replacement from xs with probability ws
def step(mod: Model, p: Parameters): (State, (Data, DenseVector[Double])) ⇒ State

A single step in the Particle Gibbs with Ancestor Sampling algorithm
A single step in the Particle Gibbs with Ancestor Sampling algorithm
mod
the model specification
p
the parameters of the model
returns
the state containing the particles, weights and running log-likelihood
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
AnyRef → Any
def transitionProbability(sampledState: DenseVector[Double], time: Double, mod: Model, p: Parameters)(conditionedState: DenseVector[Double]): Double

Calculate the transition probability from a given particle at time t-1 to the conditioned upon particle at time t
Calculate the transition probability from a given particle at time t-1 to the conditioned upon particle at time t
sampledState
a particle value at time t-1
time
the time t
mod
the DGLM model specification
p
the parameters of the model
conditionedState
the value of the conditioned state at time t
returns
the value of the log of the transition probability as a Double
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
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Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
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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 ParticleGibbsAncestor extends App

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

def ancestorResampling(mod: Model, time: Double, states: Vector[List[(Double, DenseVector[Double])]], weights: Vector[Double], condState: DenseVector[Double], p: Parameters): (List[List[(Double, DenseVector[Double])]], List[(Double, DenseVector[Double])])

def args: Array[String]

final def asInstanceOf[T0]: T0

def calcWeight(mod: Model, time: Double, xt: DenseVector[Double], xt1: DenseVector[Double], conditionedState: DenseVector[Double], y: DenseVector[Option[Double]], p: Parameters): Double

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

val executionStart: Long

def filter(n: Int, p: Parameters, mod: Model, obs: List[Data])(state: List[(Double, DenseVector[Double])]): Rand[(Double, List[(Double, DenseVector[Double])])]

def filterAll(n: Int, mod: Model, p: Parameters, obs: List[Data])(state: List[(Double, DenseVector[Double])]): State

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

def importanceWeight(sampledStates: Vector[DenseVector[Double]], weights: Vector[Double], conditionedState: DenseVector[Double], time: Double, mod: Model, p: Parameters): Vector[Double]

final def isInstanceOf[T0]: Boolean

def logSumExp(w: Vector[Double]): Vector[Double]

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

def missingState(x: DenseVector[Double], y: DenseVector[Option[Double]]): DenseVector[Double]

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def sample[A](n: Int, xs: Vector[A], ws: Vector[Double]): Vector[A]

def step(mod: Model, p: Parameters): (State, (Data, DenseVector[Double])) ⇒ State

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

def toString(): String

def transitionProbability(sampledState: DenseVector[Double], time: Double, mod: Model, p: Parameters)(conditionedState: DenseVector[Double]): Double

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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