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Normalized random measures driven by increasing additive processes. (English) Zbl 1069.62029
Summary: This paper introduces and studies a new class of nonparametric prior distributions. Random probability distribution functions are constructed via normalization of random measures driven by increasing additive processes. In particular, we present results for the distribution of means under both prior and posterior conditions and, via the use of strategic latent variables, undertake a full Bayesian analysis. Our class of priors includes the well-known and widely used mixture of a Dirichlet process.

MSC:
62F15 Bayesian inference
60G57 Random measures
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