Generates simulation of the posterior distribution of \(a\) in the mixdpcluster model for bayesian clustering. The simulation is done via Metropolis-Hastings method.
sampling_a(n = 1, a.ini, b, alpha, d_0_a, d_1_a, mu_star_n_r, n.burn = 0,
n.thin = 0, max.time = Inf, verbose = F, USING_CPP = TRUE)number of simulations to generate
initialization value
parameter \(b\) in the posterior distribution of a
parameter \(\alpha\) in the posterior distribution of a
parameter \(d_0^a\) in the posterior distribution of a
parameter \(d_1^a\) in the posterior distribution of a
bla
number of iterations in the simulation considered in the burn-in period.
number of iterations discarded between two simulated values (for thinning of the MCMC chain).
maximum allowed time for the simulation process. The function returns Error if exceeded.
if T, the function reports extra information on progress.
A list with two elements:
A numeric vector with the simulated values from the posterior distribution of a
A numeric vector with the simulated values from the posterior distribution of a
Carmona C., Nieto-Barajas L., Canale A. (2017). Model based approach for household clustering with mixed scale variables.