Gibbs_AS_posteriorCPP: Use a Gibbs sampler for the A and S variables (E-step of the...

In templateICAr: Estimate Brain Networks and Connectivity with ICA and Empirical Priors

Use a Gibbs sampler for the A and S variables (E-step of the EM)

Description

Use a Gibbs sampler for the A and S variables (E-step of the EM)

Usage

Gibbs_AS_posteriorCPP(
  nsamp,
  nburn,
  template_mean,
  template_var,
  S,
  G,
  tau_v,
  Y,
  alpha,
  final,
  return_samp
)

Arguments

nsamp	the number of posterior samples to output after burn-in
nburn	the number of posterior samples to throw away before saving
template_mean	a matrix with dimensions V x Q giving the mean value of the independent components
template_var	a matrix with dimensions V x Q giving the variance of the independent components
S	a matrix with dimensions V x Q of subject independent components
G	a Q x Q matrix of the prior covariance of A
tau_v	a length V vector with noise variance for each data location
Y	a matrix with dimensions V x T of observed BOLD data
alpha	a length Q vector of the prior mean of all rows of A
final	a boolean. Should posterior samples be returned instead of summary measures?
return_samp	a boolean. Should posterior samples be returned?

Value

List with estimates for A, S, and possibly other quantities

templateICAr documentation built on Feb. 16, 2023, 8:14 p.m.