This function implements full Gibbs sampling to simulate an MCMC sample from the posterior distribution assuming known number of mixture components.
gibbsBinMix(alpha, beta, gamma, K, m, burn, data,
thinning, z.true, outputDir)First shape parameter of the Beta prior distribution (strictly positive). Defaults to 1.
Second shape parameter of the Beta prior distribution (strictly positive). Defaults to 1.
Kmax-dimensional vector (positive) corresponding to the parameters of the Dirichlet prior of the mixture weights. Default value: rep(1,Kmax).
Number of clusters.
Number of MCMC iterations.
Burn-in period.
Binary data.
Thinning of the simulated chain.
An optional vector of cluster assignments considered as the ground-truth clustering of the observations. Useful for simulations.
Output directory.
Not really used.