Calculate the lagged difference between items in a Seq
Calculate the lagged difference between items in a Seq
a sequence of numeric values
a sequence of numeric values containing the once lagged difference
A single step of a Gibbs Sampler
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)
the observation matrix, a function from time => DenseMatrix[Double]
an array containing the state sampled from the backward sampling algorithm
an array containing the actual observations of the data
the sum of squared differences between the one step forecast and the actual observation for each time
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
the model containing the definition of the observation matrix F_t and system evolution matrix G_t
the prior distribution on the observation noise matrix, V
the prior distribution on the system noise matrix, W
the initial parameters of the Markov Chain
an array of Data containing the observed time series
a Process
Sample the (diagonal) observation noise covariance matrix from an Inverse Gamma distribution
Sample the (diagonal) observation noise covariance matrix from an Inverse Gamma distribution
an Inverse Gamma prior distribution for each variance element of the observation matrix
a sample of the DLM state
the observed values of the time series
the posterior distribution over the diagonal observation matrix
Sample the diagonal system matrix for an irregularly observed DLM