Generates simulation of the posterior distribution of the \((i,j)\) element of the \(\Omega\) matrix in the mixdpcluster model for bayesian clustering. The simulation is done via Metropolis-Hastings method.
sampling_Omega_ij(n = 1, Omega.ini, i, j, delta = 4, Z, mu_Z, Lambda,
sampling_prob, n.burn = 0, n.thin = 0, max.time = Inf, verbose = F,
USING_CPP = USING_CPP)number of simulations to be generated
matrix \(\Omega\) with an initialization value for \(\Omega_{i,j}\).
indicates the row for \(\Omega_{i,j}\)
indicates the column for \(\Omega_{i,j}\)
defines the maximum jump on each iteration of the MCMC as \(1/delta\) of the feasible interval for \(\Omega_{i,j}\)
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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 \(\Omega_{i,j} \).
A logical vector indicating whether or not omega_ij.prop was accepted.
Carmona C., Nieto-Barajas L., Canale A. (2017). Model based approach for household clustering with mixed scale variables.