Abstract
Using fractions of gamma and exponentially titled stable random variables this article develops a stick-breaking representation of a truncated normalized generalized gamma process. Sampling from the posterior of this process requires sampling from gamma titled stable random variables and we develop an algorithm to do so that is readily implemented in the open source software R. A Blocked Gibbs sampling algorithm for a Bayesian kernel mixture model is then described and we compare the performance of our algorithm with an algorithm recently proposed in the literature.
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