Compute one draw of the unconditional means in an AR(1) model with Gaussian innovations and time-dependent innovation variances. In particular, we use the sampler for the log-volatility AR(1) process with the parameter-expanded Polya-Gamma sampler. The sampler also applies to a multivariate case with independent components.
sampleLogVolMu(h, h_mu, h_phi, h_sigma_eta_t, h_sigma_eta_0, h_log_scale = 0)a list containing
the sampled mean(s) dhs_mean and
the sampled precision(s) dhs_mean_prec_j from the Polya-Gamma parameter expansion
the T x p matrix of log-volatilities
the p x 1 vector of previous means
the p x 1 vector of AR(1) coefficient(s)
the T x p matrix of log-vol innovation standard deviations
the standard deviations of initial log-vols
prior mean from scale mixture of Gaussian (Polya-Gamma) prior, e.g. log(sigma_e^2) or dhs_mean0