object GibbsSampling extends App
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- case class State (p: Parameters, state: Array[(Double, DenseVector[Double])]) extends Product with Serializable
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def
diff[A](xs: Seq[A])(implicit A: Numeric[A]): Seq[A]
Calculate the lagged difference between items in a Seq
Calculate the lagged difference between items in a Seq
- xs
a sequence of numeric values
- returns
a sequence of numeric values containing the once lagged difference
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def
dinvGammaStep(mod: Model, priorV: InverseGamma, priorW: InverseGamma, observations: Array[Data])(gibbsState: State): Rand[State]
A single step of a Gibbs Sampler
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- def gibbsMetropStep(proposal: (Parameters) ⇒ Rand[Parameters], mod: Model, priorV: InverseGamma, priorW: InverseGamma, observations: Array[Data])(gibbsState: State): Rand[State]
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- def metropSamples(proposal: (Parameters) ⇒ Rand[Parameters], mod: Model, priorV: InverseGamma, priorW: InverseGamma, initParams: Parameters, observations: Array[Data]): Process[State]
- def metropStep(mod: Model, observations: Array[Data], proposal: (Parameters) ⇒ Rand[Parameters]): (Parameters) ⇒ Rand[Parameters]
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def
observationSquaredDifference(f: (Double) ⇒ DenseMatrix[Double], state: Array[(Double, 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)
- f
the observation matrix, a function from time => DenseMatrix[Double]
- 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
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def
sample(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
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def
sampleObservationMatrix(prior: InverseGamma, f: (Double) ⇒ DenseMatrix[Double], state: Array[(Double, 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
- state
a sample of the DLM state
- observations
the observed values of the time series
- returns
the posterior distribution over the diagonal observation matrix
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def
sampleSystemMatrix(prior: InverseGamma, g: (Double) ⇒ DenseMatrix[Double], state: Array[(Double, DenseVector[Double])]): Rand[DenseMatrix[Double]]
Sample the diagonal system matrix for an irregularly observed DLM
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(Since version 2.11.0) the delayedInit mechanism will disappear