Stick-breaking processes with exchangeable length variables
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We investigate the general class of stick-breaking processes with exchangeable length variables. These generalize well-known Bayesian non-parametric priors in an unexplored direction. We give conditions to assure the respective species sampling process is discrete almost surely and the corresponding prior has full support. For a rich sub-class we find the probability that the stick-breaking weights are decreasingly ordered. A general formulae for the distribution of the latent allocation variables is derived and an MCMC algorithm is proposed for density estimation purposes.
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