**An** interesting query on (or from) X validated: given a Bernoulli mixture where the weights are known and the probabilities are jointly drawn from a Dirichlet, what is the most efficient from running a Gibbs sampler including the latent variables to running a basic Metropolis-Hastings algorithm based on the mixture representation to running a collapsed Gibbs sampler that only samples the indicator variables… I provided a closed form expression for the collapsed target, but believe that the most efficient solution is based on the mixture representation!

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## Bernoulli mixtures

Posted in pictures, Statistics, University life with tags Bernoulli mixture, cross validated, Gibbs sampler, Helvetia, Jakob Bernoulli, Metropolis-Hastings algorithm, mixtures, stamp on October 30, 2019 by xi'an## maximum of a Dirichlet vector

Posted in Books, Statistics with tags cross validated, Dirichlet distribution, LaTeX, marginalisation, order statistics, Peter Dirichlet, Stack Exchange, stamp on September 26, 2016 by xi'an**A**n intriguing question on Stack Exchange this weekend, about the distribution of max{p¹,p²,…}the maximum component of a Dirichlet vector Dir(a¹,a²,…) with arbitrary hyper-parameters. Writing the density of this random variable is feasible, using its connection with a Gamma vector, but I could not find a closed-form expression. If there is such an expression, it may follow from the many properties of the Dirichlet distribution and I’d be interested in learning about it. (Very nice stamp, by the way! I wonder if the original formula was made with LaTeX…)