A Tractable Class of σ-stable Poisson-Kingman Processes and an Effective Marginalized Sampler
نویسندگان
چکیده
We explore the use of a class of random probability measures, which we refer to as the Q class, for Bayesian nonparametric mixture modeling. This class of processes encompasses both the normalised generalised gamma and the two-parameter Poisson-Dirichlet processes allowing us to comparatively study the inferential advantages of these two processes. We propose novel marginal and conditional characterisations which allow for the development of tractable inference algorithms for the whole class. Using these characterisations we develop an efficient marginal sampler which can be used for posterior simulation in the mixture model framework. We demonstrate the efficacy of our modeling framework and sampler on both one and multidimensional data sets.
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