Run a Bayesian functional data model under a GP prior whose parameters employ a DP prior
Description
An internal function to gpdpgrow
Usage
1 2 3 
Arguments
y 
An N x T matrix of N observations of T x 1 functions 
ipr 
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

Omega_t 
A T x T matrix of squared Eucidean distances for 
Omega_s 
A 
gp_mod 
An L x 1 numeric vector denoting the selected covariance function for each
of 
jitter 
Numeric value added to diagonals of GP covariance matrix to stabilize inversion. 
gp_shape 
The shape parameter of the Gamma base distribution for the 
gp_rate 
The rate parameter of the Gamma base distribution for the 
noise_shape 
The shape parameter of the Gamma base distribution on 
noise_rate 
The rate parameter of the Gamma base distribution on 
lower 
Minimum in range of support for GP covariance parameters, 
upper 
Maximum in range of support for GP covariance parameters, 
w 
Tuning parameter for slice sampling interval width used for GP
covariance parameters, 
n_slice_iter 
Maximum number of steps to widen slice samplind width for
GP covariance parameters, 
y_index 
List object where each contains index of time points to use in 
n.iter 
The number of MCMC sampling iterations 
n.burn 
The number of warmup iterations to discard 
n.thin 
The interval or step size of postburnin samples to return 
n.tune 
The number of tuning iterations to update the slice sampler width, 
progress 
An indicator in 
Value
res A list object containing MCMC runs for all model parameters.
Note
Intended as an internal function for gpdpgrow
Author(s)
Terrance Savitsky tds151@gmail.com
See Also
gpdpgrow