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• W2060434905 abstract "This article describes posterior simulation methods for mixture models whose mixing distribution has a Normalized Random Measure prior. The methods use slice sampling ideas and introduce no truncation error. The approach can be easily applied to both homogeneous and nonhomogeneous Normalized Random Measures and allows the updating of the parameters of the random measure. The methods are illustrated on data examples using both Dirichlet and Normalized Generalized Gamma process priors. In particular, the methods are shown to be computationally competitive with previously developed samplers for Dirichlet process mixture models. Matlab code to implement these methods is available as supplemental material." @default.
• W2060434905 created "2016-06-24" @default.
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• W2060434905 date "2011-01-01" @default.
• W2060434905 modified "2023-10-01" @default.
• W2060434905 title "Posterior Simulation of Normalized Random Measure Mixtures" @default.
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• W2060434905 doi "https://doi.org/10.1198/jcgs.2010.08176" @default.
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