An internal function to gpdpgrow
gpdpPost(
y,
ipr,
Omega_t,
Omega_s,
gp_mod,
jitter,
gp_shape,
gp_rate,
noise_shape,
noise_rate,
lower,
upper,
w_star,
w,
n_slice_iter,
y_index,
n.iter,
n.burn,
n.thin,
n.tune,
M_init,
dp_shape,
dp_rate,
progress
)res A list object containing MCMC runs for all model parameters.
An N x T matrix of N observations of T x 1 functions
An optional input vector of inclusion probabilities for each observation unit in the case
the observed data were acquired through an informative sampling design, so that unbiased
inference about the population requires adjustments to the observed sample. Defaults to
ipr = rep(1,nrow(y)) indicating an iid sample.
A T x T matrix of squared Eucidean distances for T time points
A list object of length L_s, where each contains the T x T
matrix of Euclidean distances associated to each seasonal covariance term.
An L x 1 numeric vector denoting the selected covariance function for each
of L terms. gp_mod = 1 is "rq". gp_mod = 2 is "se".
gp_mod = 3 is "sn".
Numeric value added to diagonals of GP covariance matrix to stabilize inversion.
The shape parameter of the Gamma base distribution for the kappa_star
locations used to sample the DP prior on the P GP covariance parameters,
kappa, for each experimental unit.
The rate parameter of the Gamma base distribution for the kappa_star
locations used to sample the DP prior on the P GP covariance parameters,
kappa, for each experimental unit.
The shape parameter of the Gamma base distribution on tau_e, the
model noise precision parameter. Defaults to noise_shape = 3.
The rate parameter of the Gamma base distribution on tau_e, the model
noise precision parameter. Defaults to noise_rate = 1.
Minimum in range of support for GP covariance parameters, kappa.
Maximum in range of support for GP covariance parameters, kappa.
Tuning parameter for number of locations to sample not linked to observations in the auxiliary Gibbs sampler for cluster assignments.
Tuning parameter for slice sampling interval width used for GP
covariance parameters, kappa.
Maximum number of steps to widen slice samplind width for
GP covariance parameters, kappa.
List object where each contains index of time points to use in n
progressively coarser distribution for sampling kappa in tempered update steps.
The number of MCMC sampling iterations
The number of warm-up iterations to discard
The interval or step size of post-burn-in samples to return
The number of tuning iterations to update the slice sampler width, w.
Starting value of number of clusters for sampling cluster assignments.
The shape parameter for the \(\Gamma\) prior on the DP concentration parameter.
The rate parameter is set of 1.
The rate parameter for the \(\Gamma\) prior on the DP concentration parameter.
Default value is 1.
An indicator in {0,1} denoting whether to display a progress bar during model execution.
progress = 1 displays a progress bar. Defaults to progress = 1.
Terrance Savitsky tds151@gmail.com
gpdpgrow