On my book, "Machine Learning A Probabilistic Approach". It's stated that is straightforward to derive a Gibbs sampling algorithm to fit a mixture model, especially if we use conjugate priors.
So straightforward that book gives an example of fitting Mixture Gaussian without actually giving the resulting fitting algorithm.
My question is: once I have all the full conditionals of the discrete indicators, mixing weight, means and covariance, how shold I proceed for actually fitting my data? What is the algorithm that I should follow?