Mixture model fitting using conditional models and modal Gibbs sampling (opens in new tab)

Mixture models are a convenient way of modeling data using a convex combination of different parametric distributions. A new algorithm based on Gibbs sampling is used to approximate the posterior distribution of the auxiliary variables, that assign each observation to a group in the mixture, without sampling any other parameter in the model. In particular, the modes of an approximation to the full conditional distributions of the parameters ...